System and method for adaptively improving an energy delivery

ABSTRACT

Systems and methods for adaptively improving an energy delivery are disclosed. An example system includes a machine having an associated regenerative energy facility, the machine having a requirement for a compute, networking, or energy consumption task. The example system includes a controller having an energy requirement circuit to determine an amount of energy for the machine to service the task in response to the requirement for the task. The controller also includes an energy distribution circuit to adaptively improve an energy delivery, of energy produced by the associated regenerative energy facility, between the tasks.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/457,890 (Attorney Docket No. SFTX-0004-U01), filed Jun. 28, 2019,entitled “TRANSACTION-ENABLING SYSTEMS AND METHODS FOR USING A SMARTCONTRACT WRAPPER TO ACCESS EMBEDDED CONTRACT TERMS.”

U.S. patent application Ser. No. 16/457,890 (Attorney Docket No.SFTX-0004-U01) is a bypass continuation of International ApplicationSerial No. PCT/US2019/030934 (SFTX-0004-WO), filed May 6, 2019, entitled“METHODS AND SYSTEMS FOR IMPROVING MACHINES THAT AUTOMATE EXECUTION OFDISTRIBUTED LEDGER AND OTHER TRANSACTIONS IN SPOT AND FORWARD MARKETSFOR ENERGY, COMPUTE, STORAGE AND OTHER RESOURCES.”

International Application Serial No. PCT/US2019/030934 (SFTX-0004-WO)claims the benefit of priority to the following U.S. Provisional PatentApplications: Ser. No. 62/787,206 (Attorney Docket No. SFTX-0001-P01),filed Dec. 31, 2018, entitled “METHODS AND SYSTEMS FOR IMPROVINGMACHINES AND SYSTEMS THAT AUTOMATE EXECUTION OF DISTRIBUTED LEDGER ANDOTHER TRANSACTIONS IN SPOT AND FORWARD MARKETS FOR ENERGY, COMPUTE,STORAGE AND OTHER RESOURCES”; Ser. No. 62/667,550 (Attorney Docket No.SFTX-0002-P01), filed May 6, 2018, entitled “METHODS AND SYSTEMS FORIMPROVING MACHINES AND SYSTEMS THAT AUTOMATE EXECUTION OF DISTRIBUTEDLEDGER AND OTHER TRANSACTIONS IN SPOT AND FORWARD MARKETS FOR ENERGY,COMPUTE, STORAGE AND OTHER RESOURCES”; and Ser. No. 62/751,713 (AttorneyDocket No. SFTX-0003-P01), filed Oct. 29, 2018, entitled “METHODS ANDSYSTEMS FOR IMPROVING MACHINES AND SYSTEMS THAT AUTOMATE EXECUTION OFDISTRIBUTED LEDGER AND OTHER TRANSACTIONS IN SPOT AND FORWARD MARKETSFOR ENERGY, COMPUTE, STORAGE AND OTHER RESOURCES.”

Each of the foregoing applications is incorporated herein by referencein its entirety.

BACKGROUND

Machines and automated agents are increasingly involved in marketactivities, including for data collection, forecasting, planning,transaction execution, and other activities. This includes increasinglyhigh-performance systems, such as used in high-speed trading. A needexists for methods and systems that improve the machines that enablemarkets, including for increased efficiency, speed, reliability, and thelike for participants in such markets.

Many markets are increasingly distributed, rather than centralized, withdistributed ledgers like Blockchain, peer-to-peer interaction models,and micro-transactions replacing or complementing traditional modelsthat involve centralized authorities or intermediaries. A need existsfor improved machines that enable distributed transactions to occur atscale among large numbers of participants, including human participantsand automated agents.

Operations on blockchains, such as ones using cryptocurrency,increasingly require energy-intensive computing operations, such ascalculating very large hash functions on growing chains of blocks.Systems using proof-of-work, proof-of-stake, and the like have led to“mining” operations by which computer processing power is applied at alarge scale in order to perform calculations that support collectivetrust in transactions that are recorded in blockchains.

Many applications of artificial intelligence also requireenergy-intensive computing operations, such as where very large neuralnetworks, with very large numbers of interconnections, performoperations on large numbers of inputs to produce one or more outputs,such as a prediction, classification, optimization, control output, orthe like.

The growth of the Internet of Things and cloud computing platforms havealso led to the proliferation of devices, applications, and connectionsamong them, such that data centers, housing servers and other ITcomponents, consume a significant fraction of the energy consumption ofthe United States and other developed countries.

As a result of these and other trends, energy consumption has become amajor factor in utilization of computing resources, such that energyresources and computing resources (or simply “energy and compute”) havebegun to converge from various standpoints, such as requisitioning,purchasing, provisioning, configuration, and management of inputs,activities, outputs and the like. Projects have been undertaken, forexample, to place large scale computing resource facilities, such asBitcoin™ or other cryptocurrency mining operations, in close proximityto large-scale hydropower sources, such as Niagara Falls.

A major challenge for facility owners and operators is the uncertaintyinvolved in optimizing a facility, such as resulting from volatility inthe cost and availability of inputs (in particular where less stablerenewable resources are involved), variability in the cost andavailability of computing and networking resources (such as wherenetwork performance varies), and volatility and uncertainty in variousend markets to which energy and compute resources can be applied (suchas volatility in cryptocurrencies, volatility in energy markets,volatility in pricing in various other markets, and uncertainty in theutility of artificial intelligence in a wide range of applications),among other factors.

A need exists for a flexible, intelligent energy and compute facilitythat adjust in response to uncertainty and volatility, as well as for anintelligent energy and compute resource management system, such as onethat includes capabilities for data collection, storage and processing,automated configuration of inputs, resources and outputs, and learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize various relevant parameters for such afacility.

SUMMARY

Machine learning potentially enables machines that enable or interactwith automated markets to develop understanding, such as based on IoTdata, social network data, and other non-traditional data sources, andexecute transactions based on predictions, such as by participating inforward markets for energy, compute, advertising and the like.Blockchain and cryptocurrencies may support a variety of automatedtransactions, and the intersection of blockchain and AI potentiallyenables radically different transaction infrastructure. As energy isincreasingly used for computation, machines that efficiently allocateavailable energy sources among storage, compute, and base tasks becomepossible. These and other concepts are addressed by the methods andsystems disclosed herein.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includeaccessing a distributed ledger including an instruction set, tokenizingthe instruction set, interpreting an instruction set access request, andin response to the instruction set access request, providing a provableaccess to the instruction set.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includesan instruction set for a coating process.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include providing commands to a production toolof the coating process in response to the instruction set access requestand recording a transaction on the distributed ledger in response to theproviding commands to the production tool.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includesan instruction set for a 3D printing process.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include providing commands to a production toolof the 3D printing process in response to the instruction set accessrequest and recording a transaction on the distributed ledger inresponse to the providing commands to the production tool.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includesan instruction set for a semiconductor fabrication process.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include providing commands to a production toolof the semiconductor fabrication process in response to the instructionset access request and recording a transaction on the distributed ledgerin response to the providing commands to the production tool.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includes afield programmable gate array (FPGA) instruction set.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include interpreting an execution operation ofthe FPGA instruction set, and recording a transaction on the distributedledger in response to the execution operation.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includesan instruction set for a food preparation process.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include providing commands to a production toolof the food preparation process in response to the instruction setaccess request and recording a transaction on the distributed ledger inresponse to the providing commands to the production tool.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includesan instruction set for a polymer production process.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include providing commands to a production toolof the polymer production process in response to the instruction setaccess request and recording a transaction on the distributed ledger inresponse to the providing commands to the production tool.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includesan instruction set for a chemical synthesis process.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include providing commands to a production toolof the chemical synthesis process in response to the instruction setaccess request and recording a transaction on the distributed ledger inresponse to the providing commands to the production tool.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includesan instruction set for a biological production process.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include providing commands to a production toolof the biological production process in response to the instruction setaccess request and recording a transaction on the distributed ledger inresponse to the providing commands to the production tool.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includesan instruction set for a crystal fabrication process.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include providing commands to a production toolof the crystal fabrication process in response to the instruction setaccess request and recording a transaction on the distributed ledger inresponse to the providing commands to the production tool.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include interpreting an execution operation ofthe instruction set, and recording a transaction on the distributedledger in response to the execution operation.

The present disclosure describes a transaction-enabling system includinga smart contract wrapper, the contract wrapper according to onedisclosed non-limiting embodiment of the present disclosure may beconfigured to access a distributed ledger including a plurality ofembedded contract terms and a plurality of data values, interpret anaccess request value for the plurality of data values, and, in responseto the access request value, provide access to at least a portion of theplurality of data values, and commit an entity providing the accessrequest value to at least one of the plurality of embedded contractterms.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the plurality of data valuesincludes intellectual property (IP) data corresponding to a plurality ofIP assets, and wherein the plurality of embedded contract terms includesa plurality of intellectual property (IP) licensing terms for thecorresponding plurality of IP assets.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the smart contract wrapper isfurther configured to commit the entity providing the access requestvalue to corresponding IP licensing terms for accessed ones of theplurality of IP assets.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the smart contract wrapper isfurther configured to interpret an IP description value and an IPaddition request, and to add additional IP data to the plurality of datavalues in response to the IP description value and the IP additionrequest, wherein the additional IP data includes IP data correspondingto an additional IP asset.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the plurality of data valuesfurther includes a plurality of owning entities corresponding to theplurality of IP assets, and wherein the smart contract wrapper isfurther configured to apportion royalties from the plurality of IPassets to the plurality of owning entities in response to thecorresponding IP licensing terms.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the smart contract wrapper isfurther configured to: interpret an IP description value, an IP additionrequest, and an IP addition entity, add additional IP data to theplurality of data values in response to the IP description value and theIP addition request, commit the IP addition entity to the IP licensingterms, and further apportion royalties from the plurality of IP assetsto the plurality of owning entities in response to the additional IPdata and the IP addition entity.

A method for executing a smart contract wrapper for a distributed ledgermay include accessing a distributed ledger including a plurality ofembedded contract terms and a plurality of data values, interpreting anaccess request value for the plurality of data values, and, in responseto the access request value, providing access to at least a portion ofthe plurality of data values, and committing an entity providing theaccess request value to at least one of the plurality of embeddedcontract terms.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include including providing the entity providingthe access request value with a user interface including a contractacceptance input, and wherein the providing access and committing theentity is in response to a user input on the user interface.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include including providing an access option tothe user interface, and adjusting at least one of the providing accessand the committed contract terms in response to a user input on the userinterface responsive to the access option.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the plurality of data valuesincludes intellectual property (IP) data corresponding to a plurality ofIP assets, and wherein the plurality of embedded contract terms includesa plurality of intellectual property (IP) licensing terms for thecorresponding plurality of IP assets.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include committing the entity providing theaccess request value to corresponding IP licensing terms for accessedones of the plurality of IP assets.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include interpreting an IP description value andan IP addition request, and adding additional IP data to the pluralityof data values in response to the IP description value and the IPaddition request, wherein the additional IP data includes IP datacorresponding to an additional IP asset.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the plurality of data valuesfurther includes a plurality of owning entities corresponding to theplurality of IP assets, the method further including apportioningroyalties from the plurality of IP assets to the plurality of owningentities in response to the corresponding IP licensing terms.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include interpreting an IP description value, anIP addition request, and an IP addition entity, adding additional IPdata to the plurality of data values in response to the IP descriptionvalue and the IP addition request, committing the IP addition entity tothe IP licensing terms, and further apportioning royalties from theplurality of IP assets to the plurality of owning entities in responseto the additional IP data and the IP addition entity.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include updating a valuation for at least one ofthe plurality of IP assets, and updating the apportioning royalties fromthe plurality of IP assets in response to the updated valuation for theat least one of the plurality of IP assets.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include determining that at least one of theplurality of IP assets has expired, and updating the apportioningroyalties from the plurality of IP assets in response to the determiningthat the at least one of the plurality of IP assets has expired.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include determining that an owning entitycorresponding to at least one of the plurality of IP assets has changed,and updating the apportioning royalties from the plurality of IP assetsin response to the change of the owning entity.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include providing a user interface to a newowning entity of the at least one of the plurality of IP assets where anownership has changed, and committing the new owning entity to the IPlicensing terms in response to a user input on the user interface.

The present disclosure describes a transaction-enabling system includinga smart contract wrapper, the smart contract wrapper according to onedisclosed non-limiting embodiment of the present disclosure may beconfigured to access a distributed ledger including a plurality ofintellectual property (IP) licensing terms corresponding to a pluralityof IP assets, wherein the plurality of IP assets include an aggregatestack of IP, interpret an IP description value and an IP additionrequest, and, in response to the IP addition request and the IPdescription value, to add an IP asset to the aggregate stack of IP.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the smart contract wrapper isfurther configured to interpret an IP licensing value corresponding tothe IP description value, and to add the IP licensing value to theplurality of IP licensing terms in response to the IP description valueand the IP addition request.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the smart contract wrapper isfurther configured to associate at least one of the plurality of IPlicensing terms to an added IP asset.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include a data store having a copy of at leastone of the IP assets stored thereon, and wherein the aggregate stack ofIP further includes a reference to the data store for the at least oneof the IP assets.

A method according to one disclosed non-limiting embodiment of thepresent disclosure may include accessing a distributed ledger includinga plurality of intellectual property (IP) licensing terms correspondingto a plurality of IP assets, wherein the plurality of IP assets includean aggregate stack of IP, interpreting an IP description value and an IPaddition request, and, in response to the IP addition request and the IPdescription value, adding an IP asset to the aggregate stack of IP.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include interpreting an IP licensing valuecorresponding to the IP description value, and adding the IP licensingvalue to the plurality of IP licensing terms in response to the IPdescription value and the IP addition request.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include associating at least one of the pluralityof IP licensing terms to the added IP asset.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include storing at least one of the IP assets ona data store, and wherein the aggregate stack of IP includes a referenceto the stored at least one of the IP assets on the data store.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include apportioning royalties from the pluralityof IP assets to a plurality of owning entities corresponding to theaggregate stack of IP in response to the corresponding IP licensingterms.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include interpreting an IP addition entitycorresponding to the IP addition request and the IP description value,and committing the IP addition entity to the IP licensing terms.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include apportioning royalties from the pluralityof IP assets to the plurality of owning entities in response to the IPaddition entity.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include updating a valuation for at least one ofthe plurality of IP assets, and updating the apportioning royalties fromthe plurality of IP assets in response to the updated valuation for theat least one of the plurality of IP assets.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include determining that at least one of theplurality of IP assets has expired, and updating the apportioningroyalties from the plurality of IP assets in response to the determiningthat the at least one of the plurality of IP assets has expired.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include determining that an owning entitycorresponding to at least one of the plurality of IP assets has changed,and updating the apportioning royalties from the plurality of IP assetsin response to the change of the owning entity.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include providing a user interface to a newowning entity of the at least one of the plurality of IP assets where anownership has changed, and committing the new owning entity to the IPlicensing terms in response to a user input on the user interface.

The present disclosure describes a transaction-enabling system includinga controller, the controller according to one disclosed non-limitingembodiment of the present disclosure may be configured to: access adistributed ledger including an instruction set, tokenize theinstruction set, interpret an instruction set access request, and, inresponse to the instruction set access request, provide a provableaccess to the instruction set.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includesan instruction set for a coating process.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includesan instruction set for a 3D printer operation.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includesand instruction set for a semiconductor fabrication process.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includes afield programmable gate array (FPGA) instruction set.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includes afood preparation instruction set.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includes apolymer production instruction set.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includes achemical synthesis instruction set.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includes abiological production instruction set.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includesan instruction set for a crystal fabrication system.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the controller is furtherconfigured to interpret an execution operation of the instruction set,and to record a transaction on the distributed ledger in response to theexecution operation.

A method may include accessing a distributed ledger including aninstruction set, tokenizing the instruction set, interpreting aninstruction set access request, and, in response to the instruction setaccess request, providing a provable access to the instruction set.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includesan instruction set for a coating process.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include providing commands to a production toolof the coating process in response to the instruction set accessrequest.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include recording a transaction on thedistributed ledger in response to the providing commands to theproduction tool.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includesan instruction set for a 3D printing process.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include providing commands to a production toolof the 3D printing process in response to the instruction set accessrequest.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include recording a transaction on thedistributed ledger in response to the providing commands to theproduction tool.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includesan instruction set for a semiconductor fabrication process.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include providing commands to a production toolof the semiconductor fabrication process in response to the instructionset access request.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include recording a transaction on thedistributed ledger in response to the providing commands to theproduction tool.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includesfield programmable gate array (FPGA) instruction set.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include interpreting an execution operation ofthe FPGA instruction set, and recording a transaction on the distributedledger in response to the execution operation.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includesan instruction set for a food preparation process.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include providing commands to a production toolof the food preparation process in response to the instruction setaccess request.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include recording a transaction on thedistributed ledger in response to the providing commands to theproduction tool.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includesan instruction set for a polymer production process.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include providing commands to a production toolof the polymer production process in response to the instruction setaccess request.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include recording a transaction on thedistributed ledger in response to the providing commands to theproduction tool.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includesan instruction set for a chemical synthesis process.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include providing commands to a production toolof the chemical synthesis process in response to the instruction setaccess request.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include recording a transaction on thedistributed ledger in response to the providing commands to theproduction tool.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includesan instruction set for a biological production process.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include providing commands to a production toolof the biological production process in response to the instruction setaccess request.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include recording a transaction on thedistributed ledger in response to the providing commands to theproduction tool.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set includesan instruction set for a crystal fabrication process.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include providing commands to a production toolof the crystal fabrication process in response to the instruction setaccess request.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include recording a transaction on thedistributed ledger in response to the providing commands to theproduction tool.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include interpreting an execution operation ofthe instruction set, and recording a transaction on the distributedledger in response to the execution operation.

The present disclosure describes a transaction-enabling system includinga controller, the controller according to one disclosed non-limitingembodiment of the present disclosure may be configured to access adistributed ledger including executable algorithmic logic, tokenize theexecutable algorithmic logic, interpret an access request for theexecutable algorithmic logic, and, in response to the access request,provide a provable access to the executable algorithmic logic.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the controller is furtherconfigured to provide the executable algorithmic logic as a black box,and wherein the instruction set further includes an interfacedescription for the executable algorithmic logic.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the controller is furtherconfigured to interpret an execution operation of the executablealgorithmic logic, and to record a transaction on the distributed ledgerin response to the execution operation.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the executable algorithmiclogic further includes an application programming interface (API) forthe executable algorithmic logic.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure may includeaccessing a distributed ledger including executable algorithmic logic,tokenizing the executable algorithmic logic, interpreting an accessrequest for the executable algorithmic logic, and, in response to theaccess request, providing a provable access to the executablealgorithmic logic.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include providing an interface description forthe executable algorithmic logic.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include providing an application programminginterface (API) for the executable algorithmic logic.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include interpreting an execution operation ofthe executable algorithmic logic, and recording a transaction on thedistributed ledger in response to the execution operation.

The present disclosure describes a transaction-enabling system includinga controller, the controller according to one disclosed non-limitingembodiment of the present disclosure may be configured to access adistributed ledger including a firmware data value, tokenize thefirmware data value, interpret an access request for the firmware datavalue, and, in response to the access request, provide a provable accessto a firmware corresponding to the firmware data value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the controller is furtherconfigured to provide a notification to an accessor of the firmware datavalue in response to an update of the firmware data value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the controller is furtherconfigured to interpret one of a download operation or an installoperation of a firmware asset corresponding to the firmware data value,and to record a transaction on the distributed ledger in response to theone of the download operation or the install operation.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the firmware data valueincludes firmware for a component of a production process.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the component of theproduction process includes a production tool.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the production tool includes aproduction tool for a process selected from the processes consisting of:a coating process, a 3D printing process, a semiconductor fabricationprocess, a food preparation process, a polymer production process, achemical synthesis process, a biological production process, and acrystal fabrication process.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the firmware data valueincludes firmware for one of a compute resource and a networkingresource.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure may includeaccessing a distributed ledger including a firmware data value,tokenizing the firmware data value, interpreting an access request forthe firmware data value, and, in response to the access request,providing a provable access to the firmware corresponding to thefirmware data value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include providing a notification to an accessorof the firmware data value in response to an update of the firmware datavalue.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include interpreting a download operation of afirmware asset corresponding to the firmware data value, and recording atransaction on the distributed ledger in response to the downloadoperation.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include interpreting an install operation of afirmware asset corresponding to the firmware data value, and recording atransaction on the distributed ledger in response to the installoperation.

The present disclosure describes a transaction-enabling system includinga controller, the controller according to one disclosed non-limitingembodiment of the present disclosure may be configured to access adistributed ledger including serverless code logic, tokenize theserverless code logic, interpret an access request for the serverlesscode logic, and, in response to the access request, provide a provableaccess to the serverless code logic.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the controller is furtherconfigured to provide the serverless code logic as a black box, andwherein the serverless code logic further includes an interfacedescription for the serverless code logic.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the controller is furtherconfigured to interpret an execution operation of the serverless codelogic, and to record a transaction on the distributed ledger in responseto the execution operation.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the controller is furtherconfigured to interpret an execution operation of the serverless codelogic, and to record a transaction on the distributed ledger in responseto the execution operation.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the instruction set furtherincludes an application programming interface (API) for the serverlesscode logic.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure may includeaccessing a distributed ledger including serverless code logic,tokenizing the serverless code logic, interpreting an access request forthe serverless code logic, and in response to the access request,providing a provable access to the serverless code logic.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include providing an interface description forthe serverless code logic.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include providing an application programminginterface (API) for the serverless code logic.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include interpreting an execution operation ofthe serverless code logic, and recording a transaction on thedistributed ledger in response to the execution operation.

The present disclosure describes a transaction-enabling system includinga controller, the controller according to one disclosed non-limitingembodiment of the present disclosure may be configured to access adistributed ledger including an aggregated data set, interpret an accessrequest for the aggregated data set, and, in response to the accessrequest, provide a provable access to the aggregated data set, whereinthe provable access includes at least one of which parties have accessedthe aggregated data set and how many parties have accessed theaggregated data set.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the distributed ledgerincludes a block chain, and wherein the aggregated data set includes oneof a trade secret and proprietary information.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include an expert wrapper for the distributedledger, wherein the expert wrapper is configured to tokenize theaggregated data set and to validate the one of the trade secret and theproprietary information.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the distributed ledgerincludes a set of instructions, and wherein the controller is furtherconfigured to interpret an instruction update value, and to update theset of instructions in response to the access request and theinstruction update value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include a smart wrapper for the distributedledger, wherein the smart wrapper is configured to allocate a pluralityof sub-sets of instructions to the distributed ledger as the aggregateddata set, and manage access to the plurality of sub-sets of instructionsin response to the access request.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the controller is furtherconfigured to interpret an access of one of the plurality of sub-sets ofinstructions, and to record a transaction on the distributed ledger inresponse to the access.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the controller is furtherconfigured to interpret an execution operation of one of the pluralityof sub-sets of instructions, and to record a transaction on thedistributed ledger in response to the access.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure may includeaccessing a distributed ledger including an aggregated data set,interpreting an access request for the aggregated data set, and, inresponse to the access request, providing a provable access to theaggregated data set, wherein the provable access includes at least oneof which parties have accessed the aggregated data set and how manyparties have accessed the aggregated data set.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include operating an expert wrapper for thedistributed ledger, wherein the expert wrapper is configured to tokenizethe aggregated data set and to validate at least one of trade secret orproprietary information of the aggregated data set.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the distributed ledger furtherincludes a set of instructions, the method further includinginterpreting an instruction update value, and updating the set ofinstructions in response to the access request and the instructionupdate value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include allocating a plurality of sub-sets ofinstructions to the distributed ledger as the aggregated data set, andmanaging access to the plurality of sub-sets of instructions in responseto the access request.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include interpreting an access of one of theplurality of sub-sets of instructions, and recording a transaction onthe distributed ledger in response to the access.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include interpreting an execution operation ofone of the plurality of sub-sets of instructions, and recording atransaction on the distributed ledger in response to the access.

The present disclosure describes a transaction-enabling system includinga controller, the controller according to one disclosed non-limitingembodiment of the present disclosure may access a distributed ledgerincluding a plurality of intellectual property (IP) data correspondingto a plurality of IP assets, wherein the plurality of IP assets includean aggregate stack of IP, tokenize the IP data, interpret a distributedledger operation corresponding to at least one of the plurality of IPassets, determine an analytic result value in response to thedistributed ledger operation and the tokenized IP data, and provide areport of the analytic result value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the distributed ledgeroperation includes at least one operation selected from the operationsconsisting of accessing IP data corresponding to one of the plurality ofIP assets, executing a process utilizing IP data corresponding to one ofthe plurality of IP assets, adding IP data corresponding to anadditional IP asset to the aggregate stack of IP, and removing IP datacorresponding to one of the plurality of IP assets.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the analytic result valueincludes at least one result value selected from the result valuesconsisting of a number of access events corresponding to at least one ofthe plurality of IP assets, statistical information corresponding toaccess events for a plurality of IP assets, a distribution of theplurality of IP assets according to access event rates, one of accesstimes or processing times corresponding to at least one of the pluralityof IP assets, and unique entity access events corresponding to at leastone of the plurality of IP assets.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure may includeaccessing a distributed ledger including a plurality of IP datacorresponding to a plurality of IP assets, wherein the plurality of IPassets include an aggregate stack of IP, tokenizing the IP data,interpreting a distributed ledger operation corresponding to at leastone of the plurality of IP assets, determining an analytic result valuein response to the distributed ledger operation and the tokenized IPdata, and providing a report of the analytic result value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein determining the analyticresult value includes determining a number of access eventscorresponding to at least one of the plurality of IP assets.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein determining the analyticresult value includes determining one of an access time or a processingtime corresponding to at least one of the plurality of IP assets.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein determining the analyticresult value includes determining a number of unique entity accessevents corresponding to at least one of the plurality of IP assets.

The present disclosure describes a transaction-enabling system includinga controller, the controller according to one disclosed non-limitingembodiment of the present disclosure can be configured to interpret aresource utilization requirement for a task system having at least oneof a compute task, a network task, or a core task; interpret a pluralityof external data sources, wherein the plurality of external data sourcesincludes at least one data source outside of the task system; operate anexpert system to predict a forward market price for a resource inresponse to the resource utilization requirement and the plurality ofexternal data sources; and execute a transaction on a resource market inresponse to the predicted forward market price.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the plurality of external datasources includes an internet-of-things (IoT) data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the forward market priceincludes a forward market price for a network bandwidth resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the forward market priceincludes a forward market price for a spectrum resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the plurality of external datasources includes a social media data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the forward market priceincludes a forward market price for a network bandwidth resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the forward market priceincludes a forward market price for a spectrum resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource utilizationrequirement includes a first resource, and wherein the resource of theforward market price includes at least one of: the first resource, and asecond resource that can be substituted for the first resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the controller is furtherconfigured to operate the expert system to determine a substitution costof the second resource, and to execute the transaction on the resourcemarket further in response to the substitution cost of the secondresource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system is furtherconfigured to determine at least a portion of the substitution cost ofthe second resource as an operational change cost for the task system.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource utilizationrequirement includes at least one resource selected from the resourcesconsisting of: a compute resource, a network bandwidth resource, aspectrum resource, a data storage resource, an energy resource, and anenergy credit resource.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includeinterpreting a resource utilization requirement for a task system havingat least one of a compute task, a network task, or a core task;interpreting a plurality of external data sources, wherein the pluralityof external data sources includes at least one data source outside ofthe task system; operating an expert system to predict a forward marketprice for a resource in response to the resource utilization requirementand the plurality of external data sources; and executing a transactionon a resource market in response to the predicted forward market price.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the plurality of external datasources includes an internet-of-things (IoT) data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the forward market priceincludes a forward market price for a network bandwidth resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the forward market priceincludes a forward market price for a spectrum resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the plurality of external datasources includes a social media data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the forward market priceincludes a forward market price for a network bandwidth resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the forward market priceincludes a forward market price for a spectrum resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource utilizationrequirement includes a first resource, and wherein the resource of theforward market price includes at least one of: the first resource, and asecond resource that can be substituted for the first resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include operating the expert system to determinea substitution cost of the second resource, and executing thetransaction on the resource market further in response to thesubstitution cost of the second resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include determining at least a portion of thesubstitution cost of the second resource as an operational change costfor the task system.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource utilizationrequirement includes at least one resource selected from the resourcesconsisting of: a compute resource, a network bandwidth resource, aspectrum resource, a data storage resource, an energy resource, and anenergy credit resource.

The present disclosure describes a transaction-enabling system includinga controller, the controller according to one disclosed non-limitingembodiment of the present disclosure can be configured to interpret aresource utilization requirement for a task system having at least oneof a compute task, a network task, or a core task; interpret abehavioral data source; operate a machine to forecast a forward marketvalue for a resource in response to the resource utilization requirementand the behavioral data source; and perform one of adjusting anoperation of the task system or executing a transaction in response tothe forecast of the forward market value for the resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the forward market value forthe resource includes a forward market for energy prices.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the behavioral data sourceincludes an automated agent behavioral data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the behavioral data sourceincludes a human behavioral data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the behavioral data sourceincludes a business entity behavioral data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the forward market value forthe resource includes a forward market for a spectrum resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the behavioral data sourceincludes an automated agent behavioral data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the behavioral data sourceincludes a human behavioral data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the behavioral data sourceincludes a business entity behavioral data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the forward market value forthe resource includes a forward market for a compute resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the behavioral data sourceincludes an automated agent behavioral data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the behavioral data sourceincludes a human behavioral data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the behavioral data sourceincludes a business entity behavioral data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the forward market value forthe resource includes a forward market for an energy credit resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the behavioral data sourceincludes an automated agent behavioral data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the behavioral data sourceincludes a human behavioral data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the behavioral data sourceincludes a business entity behavioral data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource utilizationrequirement includes a first resource, and wherein the resource of theforward market value includes at least one of: the first resource, and asecond resource that can be substituted for the first resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the controller is furtherconfigured to operate the machine to determine a substitution cost ofthe second resource, and to perform the one of adjusting the operationof the task system or executing the transaction further in response tothe substitution cost of the second resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the machine is furtherconfigured to determine at least a portion of the substitution cost ofthe second resource as an operational change cost for the task system.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the performing includesexecuting the transaction, wherein the transaction includes one ofpurchasing or selling one of the first resource or the second resourceon a market for at least one of the first resource or the secondresource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource utilizationrequirement includes at least one resource selected from the resourcesconsisting of: a compute resource, a network bandwidth resource, aspectrum resource, a data storage resource, an energy resource, and anenergy credit resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the performing includesadjusting the operation of the task system, and wherein the adjustingfurther includes at least one operation selected from the operationsconsisting of: adjusting operations of the task system to increase orreduce the resource utilization requirement, adjusting operations of thetask system to time shift at least a portion of the resource utilizationrequirement, adjusting operations of the task system to substituteutilization of a first resource for utilization of a second resource,and accessing an external provider to provide at least a portion of atleast one of the compute task, the network task, or the core task.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the performing includesexecuting the transaction, wherein the transaction includes one ofpurchasing or selling the resource on a market for the resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the market for the resourceincludes a forward market for the resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the market for the resourceincludes a spot market for the resource.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includeinterpreting a resource utilization requirement for a task system havingat least one of a compute task, a network task, or a core task;interpreting a behavioral data source; operating a machine to forecast aforward market value for a resource in response to the resourceutilization requirement and the behavioral data source; and performingone of adjusting an operation of the task system or executing atransaction in response to the forecast of the forward market value forthe resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the forward market value forthe resource includes a forward market for energy prices.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the behavioral data sourceincludes an automated agent behavioral data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the behavioral data sourceincludes a human behavioral data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the behavioral data sourceincludes a business entity behavioral data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the forward market value forthe resource includes a forward market for a spectrum resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the behavioral data sourceincludes an automated agent behavioral data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the behavioral data sourceincludes a human behavioral data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the behavioral data sourceincludes a business entity behavioral data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the forward market value forthe resource includes a forward market for a compute resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the behavioral data sourceincludes an automated agent behavioral data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the behavioral data sourceincludes a human behavioral data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the behavioral data sourceincludes a business entity behavioral data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the forward market value forthe resource includes a forward market for an energy credit resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the behavioral data sourceincludes an automated agent behavioral data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the behavioral data sourceincludes a human behavioral data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the behavioral data sourceincludes a business entity behavioral data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource utilizationrequirement includes a first resource, and wherein the resource of theforward market value includes at least one of: the first resource, and asecond resource that can be substituted for the first resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include operating the machine to determine asubstitution cost of the second resource, and performing the one ofadjusting the operation of the task system or executing the transactionfurther in response to the substitution cost of the second resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include determining at least a portion of thesubstitution cost of the second resource as an operational change costfor the task system.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the performing includesexecuting the transaction, wherein the transaction includes one ofpurchasing or selling one of the first resource or the second resourceon a market for at least one of the first resource or the secondresource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource utilizationrequirement includes at least one resource selected from the resourcesconsisting of: a compute resource, a network bandwidth resource, aspectrum resource, a data storage resource, an energy resource, and anenergy credit resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the performing includesadjusting the operation of the task system, and wherein the adjustingfurther includes at least one operation selected from the operationsconsisting of: adjusting operations of the task system to increase orreduce the resource utilization requirement, adjusting operations of thetask system to time shift at least a portion of the resource utilizationrequirement, adjusting operations of the task system to substituteutilization of a first resource for utilization of a second resource,and accessing an external provider to provide at least a portion of atleast one of the compute task, the network task, or the core task.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the performing includesexecuting the transaction, wherein the transaction includes one ofpurchasing or selling the resource on a market for the resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the market for the resourceincludes a forward market for the resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the market for the resourceincludes a spot market for the resource.

The present disclosure describes a transaction-enabling system includinga controller, the controller according to one disclosed non-limitingembodiment of the present disclosure can be configured to interpret aresource utilization requirement for a task system having at least oneof a compute task, a network task, or a core task; interpret a pluralityof external data sources, wherein the plurality of external data sourcesincludes at least one data source outside of the task system; operate anexpert system to predict a forward market price for a resource inresponse to the resource utilization requirement and the plurality ofexternal data sources; and execute a cryptocurrency transaction on aresource market in response to the predicted forward market price.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the plurality of external datasources includes an internet-of-things (IoT) data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the forward market priceincludes a forward market price for a network bandwidth resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the forward market priceincludes a forward market price for a spectrum resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the plurality of external datasources includes a social media data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the forward market priceincludes a forward market price for a network bandwidth resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the forward market priceincludes a forward market price for a spectrum resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource utilizationrequirement includes a first resource, and wherein the resource of theforward market price includes at least one of: the first resource, and asecond resource that can be substituted for the first resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the controller is furtherconfigured to operate the expert system to determine a substitution costof the second resource, and to execute the cryptocurrency transaction onthe resource market further in response to the substitution cost of thesecond resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system is furtherconfigured to determine at least a portion of the substitution cost ofthe second resource as an operational change cost for the task system.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource utilizationrequirement includes at least one resource selected from the resourcesconsisting of: a compute resource, a network bandwidth resource, aspectrum resource, a data storage resource, an energy resource, and anenergy credit resource.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includeinterpreting a resource utilization requirement for a task system havingat least one of a compute task, a network task, or a core task;interpreting a plurality of external data sources, wherein the pluralityof external data sources includes at least one data source outside ofthe task system; operating an expert system to predict a forward marketprice for a resource in response to the resource utilization requirementand the plurality of external data sources; and executing acryptocurrency transaction on a resource market in response to thepredicted forward market price.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the plurality of external datasources includes an internet-of-things (IoT) data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the forward market priceincludes a forward market price for a network bandwidth resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the forward market priceincludes a forward market price for a spectrum resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the plurality of external datasources includes a social media data source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the forward market priceincludes a forward market price for a network bandwidth resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the forward market priceincludes a forward market price for a spectrum resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource utilizationrequirement includes a first resource, and wherein the resource of theforward market price includes at least one of: the first resource, and asecond resource that can be substituted for the first resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include operating the expert system to determinea substitution cost of the second resource, and executing thecryptocurrency transaction on the resource market further in response tothe substitution cost of the second resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include determining at least a portion of thesubstitution cost of the second resource as an operational change costfor the task system.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource utilizationrequirement includes at least one resource selected from the resourcesconsisting of: a compute resource, a network bandwidth resource, aspectrum resource, a data storage resource, an energy resource, and anenergy credit resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include operating the expert system to predict aforward market price for a plurality of forward market time frames.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the executing a cryptocurrencytransaction on a resource market in response to the predicted forwardmarket price includes providing for an improved cost of operation of thetask system.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource utilizationrequirement includes a first resource, and wherein the method furtherincludes: determining a second resource that can be substituted for thefirst resource, wherein operating the expert system to predict theforward market price for the plurality of forward market time framesfurther includes predicting the forward market price for both of thefirst resource and the second resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the executing thecryptocurrency transaction on the resource market in response to thepredicted forward market price includes providing for an improved costof operation of the task system.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the providing for an improvedcost of operation of the task system further includes determining aresource utilization profile, wherein the resource utilization profileincludes a utilization of each of the first resource and the secondresource.

The present disclosure describes a transaction-enabling system, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a machine having at least one of a compute taskrequirement, a networking task requirement, and an energy consumptiontask requirement; and a controller, comprising: a resource requirementcircuit structured to determine an amount of a resource for the machineto service at least one of the compute task requirement, the networkingtask requirement, and the energy consumption task requirement; a forwardresource market circuit structured to access a forward resource market;a resource market circuit structured to access a resource market; and aresource distribution circuit structured to execute a transaction of theresource on at least one of the resource market or the forward resourcemarket in response to the determined amount of the resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit is further structured to adaptively improve at least one of anoutput of the machine or a resource utilization of the machine.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit further comprises at least one of a machine learning component,an artificial intelligence component, or a neural network component.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises acompute resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy credit resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include situations wherein the resourcerequirement circuit is further structured to determine a second amountof a second resource for the machine to service at least one of thecompute task requirement, the networking task requirement, and theenergy consumption task requirement; and wherein the resourcedistribution circuit is further structured to execute a firsttransaction of the first resource on one of the resource market or theforward resource market, and to execute a second transaction of thesecond resource on the other of the one of the resource market or theforward resource market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the second resource comprisesa substitute resource for the first resource during at least a portionof an operating condition for the machine.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the forward resource marketcomprises a futures market for the resource at a first time scale, andwherein the resource market comprises one of: a spot market for theresource, or a futures market for the resource at a second time scale.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the transaction comprises atleast one transaction type selected from the transaction typesconsisting of: a sale of the resource; a purchase of the resource; ashort sale of the resource; a call option for the resource; a put optionfor the resource; and any of the foregoing with regard to at least oneof a substitute resource or a correlated resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit is further structured to determine at least one of a substituteresource or a correlated resource, and to further execute at least onetransaction of the at least one of the substitute resource or thecorrelated resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit is further structured to execute the at least one transaction ofthe at least one of the substitute resource or the correlated resourceas a replacement for the transaction of the resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit is further structured to execute the at least one transaction ofthe at least one of the substitute resource or the correlated resourcein concert with the transaction of the resource.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includedetermining an amount of a first resource for a machine to service atleast one of a compute task requirement, a networking task requirement,and an energy consumption task requirement; accessing a forward resourcemarket; accessing a resource market; and executing a transaction of thefirst resource on at least one of the resource market or the forwardresource market in response to the determined amount of the firstresource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include determining a second amount of a secondresource for the machine to service at least one of the compute taskrequirement, the networking task requirement, and the energy consumptiontask requirement; and executing a first transaction of the firstresource on one of the resource market or the forward resource market,and executing a second transaction of the second resource on the otherof the at least one of the resource market or the forward resourcemarket.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include determining at least one of a substituteresource or a correlated resource, and executing at least onetransaction of the at least one of the substitute resource or thecorrelated resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include executing the at least one transaction ofthe at least one of the substitute resource or the correlated resourceas a replacement for the transaction of the resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include executing the at least one transaction ofthe at least one of the substitute resource or the correlated resourcein concert with the transaction of the resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein determining the correlatedresource comprises at least one operation selected from the operationsconsisting of: determining the correlated resource for the machine as aresource to service alternate tasks that provide acceptablefunctionality for the machine; determining the correlated resource as aresource that is expected to be correlated with the resource in regardto at least one of a price or an availability; and determining thecorrelated resource as a resource that is expected to have acorresponding price change with the resource, such that a subsequentsale of the correlated resource combined with a spot market purchase ofthe resource provides for a planned economic outcome.

The present disclosure describes a transaction-enabling system, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a transaction detection circuit structured tointerpret a transaction request value, wherein the transaction requestvalue includes a transaction description for one of a proposed or animminent transaction, and wherein the transaction description includes acryptocurrency type value and a transaction amount value, a transactionlocator circuit structured to determine a transaction location parameterin response to the transaction request value, wherein the transactionlocation parameter includes at least one of a transaction geographicvalue or a transaction jurisdiction value, and a transaction executioncircuit structured to provide a transaction implementation command inresponse to the transaction location parameter.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the transaction locatorcircuit is further structured to determine the transaction locationparameter based on a tax treatment of the one of the proposed orimminent transaction.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the transaction locatorcircuit is further structured to select the one of the transactiongeographic value or the transaction jurisdiction value from a pluralityof available geographic values or jurisdiction values that provides animproved tax treatment relative to a nominal one of the plurality ofavailable geographic values or jurisdiction values.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the transaction locatorcircuit is further structured to determine the transaction locationparameter in response to a tax treatment of at least one of thecryptocurrency type value or a type of the one of the proposed orimminent transaction.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the transaction request valuefurther includes a transaction location value, and wherein thetransaction locator circuit is further structured to provide thetransaction location parameter as the transaction location value inresponse to determining that a tax treatment of the one of the proposedor imminent transaction meets a threshold tax treatment value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the transaction locatorcircuit is further structured to operate an expert system configured touse machine learning to continuously improve the determination of thetransaction location parameter relative to a tax treatment oftransactions processed by the controller.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the transaction locatorcircuit is further structured to operate an expert system, wherein theexpert system is configured to aggregate regulatory information forcryptocurrency transactions from a plurality of jurisdictions, and tocontinuously improve the determination of the transaction locationparameter based on the aggregated regulatory information.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system is furtherconfigured to use machine learning to continuously improve thedetermination of the transaction location parameter relative tosecondary jurisdictional costs related to the cryptocurrencytransactions.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system is furtherconfigured to use machine learning to continuously improve thedetermination of the transaction location parameter relative to atransaction speed for the cryptocurrency transactions.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system is furtherconfigured to use machine learning to continuously improve thedetermination of the transaction location parameter relative to a taxtreatment for the cryptocurrency transactions.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system is furtherconfigured to use machine learning to continuously improve thedetermination of the transaction location parameter relative to afavorability of contractual terms related to the cryptocurrencytransactions.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the expert system is furtherconfigured to use machine learning to continuously improve thedetermination of the transaction location parameter relative to acompliance of the cryptocurrency transactions within the aggregatedregulatory information.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include a transaction engine responsive to thetransaction implementation command.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includeinterpreting a transaction request value, wherein the transactionrequest value includes a transaction description for one of a proposedor an imminent transaction, and wherein the transaction descriptionincludes a cryptocurrency type value and a transaction amount value,determining a transaction location parameter in response to thetransaction request value, wherein the transaction location parameterincludes at least one of a transaction geographic value or a transactionjurisdiction value, and providing a transaction implementation commandin response to the transaction location parameter.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include determining the transaction locationparameter based on a tax treatment of the one of the proposed orimminent transaction.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include selecting at least one of the transactiongeographic value or the transaction jurisdiction value from a pluralityof available geographic values or jurisdiction values that provides animproved tax treatment relative to a nominal one of the plurality ofavailable geographic values or jurisdiction values.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include determining the transaction locationparameter in response to a tax treatment of at least one of thecryptocurrency type value or a type of the one of the proposed orimminent transaction.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the transaction request valuefurther includes a transaction location value, the method furtherincluding providing the transaction location parameter as thetransaction location value in response to determining that a taxtreatment of the one of the proposed or imminent transaction meets athreshold tax treatment value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include aggregating regulatory information forcryptocurrency transactions from a plurality of jurisdictions, andcontinuously improving the determination of the transaction locationparameter based on the aggregated regulatory information.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include applying machine learning to continuouslyimprove the determination of the transaction location parameter relativeto at least one parameter selected from the parameters consisting of:secondary jurisdictional costs related to the transactions, atransaction speed for the transactions, a tax treatment for thetransactions, a favorability of contractual terms related to thetransactions, and a compliance of the transactions within the aggregatedregulatory information.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includeinterpreting a transaction request value, wherein the transactionrequest value includes a transaction description for one of a proposedor an imminent transaction, and wherein the transaction descriptionincludes a cryptocurrency type value and a transaction amount value,determining a transaction location parameter in response to thetransaction request value, wherein the transaction location parameterincludes at least one of a transaction geographic value or a transactionjurisdiction value, and executing a transaction in response to thetransaction location parameter.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include determining the transaction locationparameter based on a tax treatment of the one of the proposed orimminent transaction.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include selecting the one of the transactiongeographic value or the transaction jurisdiction value from a pluralityof available geographic values or jurisdiction values that provides animproved tax treatment relative to a nominal one of the plurality ofavailable geographic values or jurisdiction values.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include aggregating regulatory information forcryptocurrency transactions from a plurality of jurisdictions, andcontinuously improving the determination of the transaction locationparameter based on the aggregated regulatory information.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include applying machine learning to continuouslyimprove the determination of the transaction location parameter relativeto at least one parameter selected from the parameters consisting of:secondary jurisdictional costs related to the transactions, atransaction speed for the transactions, a tax treatment for thetransactions, a favorability of contractual terms related to thetransactions, and a compliance of the transactions within the aggregatedregulatory information.

The present disclosure describes a transaction-enabling system, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a controller. The controller including a smartwrapper structured to interpret a transaction request value from a user,wherein the transaction request value includes a transaction descriptionfor an incoming transaction, and wherein the transaction descriptionincludes a transaction amount value and at least one of a cryptocurrencytype value and a transaction location value, a transaction locatorcircuit structured to determine a transaction location parameter inresponse to the transaction request value and further in response to aplurality of tax treatment values corresponding to a plurality oftransaction locations, wherein the transaction location parameterincludes at least one of a transaction geographic value or a transactionjurisdiction value, and wherein the smart wrapper is further structuredto direct an execution of the incoming transaction in response to thetransaction location parameter.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the transaction locatorcircuit is further structured to select an available one of theplurality of transaction locations having a favorable tax treatmentvalue.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the transaction location valueincludes at least one location value corresponding to: a location of apurchaser of the transaction, a location of a seller of the transaction,a location of a delivery of a product or service of the transaction, alocation of a supplier of a product or service of the transaction, aresidence location of one of the purchaser, seller, or supplier of thetransaction, and a legally available location for the transaction.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includeinterpreting a transaction request value from a user, wherein thetransaction request value includes a transaction description for anincoming transaction, and wherein the transaction description includes atransaction amount value and at least one of a cryptocurrency type valueand a transaction location value, determining a transaction locationparameter in response to the transaction request value and further inresponse to a plurality of tax treatment values corresponding to aplurality of transaction locations, wherein the transaction locationparameter includes at least one of a transaction geographic value or atransaction jurisdiction value, and directing an execution of theincoming transaction in response to the location parameter.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include selecting an available one of theplurality of transaction locations having a favorable tax treatmentvalue.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein determining the transactionlocation value includes selecting the transaction location value from alist of locations consisting of: a location of a purchaser of thetransaction, a location of a seller of the transaction, a location of adelivery of a product or service of the transaction, a location of asupplier of a product or service of the transaction, a residencelocation of one of the purchaser, seller, or supplier of thetransaction, and a legally available location for the transaction.

The present disclosure describes a transaction-enabling system, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a controller. The controller according to onedisclosed non-limiting embodiment of the present disclosure can includea transaction detection circuit structured to interpret a plurality oftransaction request values, wherein each transaction request valueincludes a transaction description for one of a proposed or an imminenttransaction, and wherein the transaction description includes acryptocurrency type value and a transaction amount value, a transactionsupport circuit structured to interpret a support resource descriptionincluding at least one supporting resource for the plurality oftransactions, a support utilization circuit structured to operate anexpert system, wherein the expert system is configured to use machinelearning to continuously improve at least one execution parameter forthe plurality of transactions relative to the support resourcedescription, and a transaction execution circuit structured to commandexecution of the plurality of transactions in response to the improvedat least one execution parameter.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the support resourcedescription includes an energy price description for an energy sourceavailable to power the execution of the plurality of transactions.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the energy price descriptionincludes at least one of a forward price prediction and a spot price forthe energy source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the support resourcedescription includes a plurality of energy sources available to powerthe execution of the plurality of transactions.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the support resourcedescription includes at least one of a state of charge and a chargecycle cost description for an energy storage source available to powerthe execution of the plurality of transactions.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the energy storage sourceincludes a battery, and wherein the expert system is further configuredto user machine learning to improve at least one parameter selected fromthe parameters consisting of: a battery energy transfer efficiencyvalue, a battery life value, and a battery lifetime utilization costvalue.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includeinterpreting a plurality of transaction request values, wherein eachtransaction request value includes a transaction description for one ofa proposed or an imminent transaction, and wherein the transactiondescription includes a cryptocurrency type value and a transactionamount value, interpreting a support resource description including atleast one supporting resource for the plurality of transactions,operating an expert system, wherein the expert system is configured touse machine learning to continuously improve at least one executionparameter for the plurality of transactions relative to the supportresource description, and commanding execution of the plurality oftransactions in response to the improved at least one executionparameter.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the commanding executionincludes utilizing the continuously improved at least one executionparameter.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include commanding execution of a firsttransaction in response to the at least one execution parameter, whereinthe continuously improving the at least one execution parameter includesupdating the at least one execution parameter, the method furtherincluding commanding execution of a second transaction using the updatedat least one execution parameter.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the support resourcedescription includes an energy price description for an energy sourceavailable to power the execution of the plurality of transactions.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the energy price descriptionincludes at least one of a forward price prediction and a spot price forthe energy source.

The present disclosure describes a transaction-enabling system, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a controller. The controller according to onedisclosed non-limiting embodiment of the present disclosure can includean attention market access circuit structured to interpret a pluralityof attention-related resources available on an attention market, anintelligent agent circuit structured to determine an attention-relatedresource acquisition value based on a cost parameter of at least one ofthe plurality of attention-related resources, and an attentionacquisition circuit structured to solicit an attention-related resourcein response to the attention-related resource acquisition value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the attention acquisitioncircuit is further structured to perform the soliciting theattention-related resource by performing at least one operation selectedfrom the operations consisting of purchasing the attention-relatedresource from the attention market, selling the attention-relatedresource to the attention market, making an offer to sell theattention-related resource to a second intelligent agent, and making anoffer to purchase the attention-related resource to the secondintelligent agent.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the plurality ofattention-related resources includes at least one resource selected froma list consisting of an advertising placement, a search listing, akeyword listing, a banner advertisement, a video advertisement, anembedded video advertisement, a panel activity participation, a surveyactivity participation, a trial activity participation, and a pilotactivity placement or participation.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the attention market includesa spot market for at least one of the plurality of attention-relatedresources.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the cost parameter of at leastone of the plurality of attention-related resources includes a futurepredicted cost of the at least one of the plurality of attention-relatedresources, and wherein the intelligent agent circuit is furtherstructured to determine the attention-related resource acquisition valuein response to a comparison of a first cost on the spot market with thecost parameter.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the attention market includesa forward market for at least one of the plurality of attention-relatedresources, and wherein the cost parameter of the at least one of theplurality of attention-related resources includes a predicted futurecost.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the cost parameter of at leastone of the plurality of attention-related resources includes a futurepredicted cost of the at least one of the plurality of attention-relatedresources, and wherein the intelligent agent circuit is furtherstructured to determine the attention-related resource acquisition valuein response to a comparison of a first cost on the forward market withthe cost parameter.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the intelligent agent circuitis further structured to determine the attention-related resourceacquisition value in response to the cost parameter of the at least oneof the plurality of attention-related resources having a value that isoutside of an expected cost range for the at least one of the pluralityof attention-related resources.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the intelligent agent circuitis further structured to determine the attention-related resourceacquisition value in response to a function of the cost parameter of theat least one of the plurality of attention-related resources, and aneffectiveness parameter of the at least one of the plurality ofattention-related resources.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the controller furtherincludes an external data circuit structured to interpret a social mediadata source, and wherein the intelligent agent circuit is furtherstructured to determine, in response to the social media data source, atleast one of a future predicted cost of the at least one of theplurality of attention-related resources, and to utilize the futurepredicted cost as the cost parameter, and the effectiveness parameter ofthe at least one of the plurality of attention-related resources.

The present disclosure describes a system, the system according to onedisclosed non-limiting embodiment of the present disclosure can includea fleet of machines, each one of the fleet of machines including a tasksystem having a core task and at least one of a compute task or anetwork task, a controller, including an attention market access circuitstructured to interpret a plurality of attention-related resourcesavailable on an attention market, and an intelligent agent circuitstructured to determine an attention-related resource acquisition valuebased on a cost parameter of at least one of the plurality ofattention-related resources, and further based on the core task for acorresponding machine of the fleet of machines, an attention purchaseaggregating circuit structured to determine an aggregateattention-related resource purchase value in response to the pluralityof attention-related resource acquisition values from each intelligentagent circuit corresponding to each machine of the fleet of themachines, and an attention acquisition circuit structured to purchase anattention-related resource in response to the aggregateattention-related resource purchase value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the attention purchaseaggregating circuit is positioned at a location selected from thelocations consisting of at least partially distributed on a plurality ofthe controllers corresponding to machines of the fleet of machines, on aselected controller corresponding to one of the machines of the fleet ofmachines, and on a system controller communicatively coupled to theplurality of the controllers corresponding to machines of the fleet ofmachines.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the attention purchaseacquisition circuit is positioned at a location selected from thelocations consisting of at least partially distributed on a plurality ofthe controllers corresponding to machines of the fleet of machines, on aselected controller corresponding to one of the machines of the fleet ofmachines, and on a system controller communicatively coupled to theplurality of the controllers corresponding to machines of the fleet ofmachines.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includeinterpreting a plurality of attention-related resources available on anattention market, determining an attention-related resource acquisitionvalue based on a cost parameter of at least one of the plurality ofattention-related resources, soliciting an attention-related resource inresponse to the attention-related resource acquisition value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include performing the soliciting theattention-related resource by performing at least one operation selectedfrom the operations consisting of purchasing the attention-relatedresource from the attention market, selling the attention-relatedresource to the attention market, making an offer to sell theattention-related resource to a second intelligent agent, and making anoffer to purchase the attention-related resource to the secondintelligent agent.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the cost parameter of at leastone of the plurality of attention-related resources includes a futurepredicted cost of the at least one of the plurality of attention-relatedresources, the method further including determining theattention-related resource acquisition value in response to a comparisonof a first cost on the attention market with the cost parameter.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include interpreting a social media data sourceand determining, in response to the social media data source, at leastone of a future predicted cost of the at least one of the plurality ofattention-related resources, and to utilize the future predicted cost asthe cost parameter, and an effectiveness parameter of the at least oneof the plurality of attention-related resources, and wherein thedetermining the attention-related resource acquisition value is furtherbased on the at least one of the future predicted cost or theeffectiveness parameter.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includeinterpreting a plurality of attention-related resources available on anattention market, determining an attention-related resource acquisitionvalue for each machine of a fleet of machines based on a cost parameterof at least one of the plurality of attention-related resources, andfurther based on a core task for each of a corresponding machine of thefleet of machines, determining an aggregate attention-related resourcepurchase value in response to the plurality of attention-relatedresource acquisition values corresponding to each machine of the fleetof the machines, and an attention acquisition circuit structured topurchase an attention-related resource in response to the aggregateattention-related resource purchase value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the cost parameter of at leastone of the plurality of attention-related resources includes a futurepredicted cost of the at least one of the plurality of attention-relatedresources, the method further including determining eachattention-related resource acquisition value in response to a comparisonof a first cost on a spot market for attention-related resources withthe cost parameter.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include interpreting a social media data sourceand determining, in response to the social media data source, at leastone of a future predicted cost of the at least one of the plurality ofattention-related resources, and to utilize the future predicted cost asthe cost parameter, and an effectiveness parameter of the at least oneof the plurality of attention-related resources, and wherein thedetermining the attention-related resource acquisition value is furtherbased on the at least one of the future predicted cost or theeffectiveness parameter.

The present disclosure describes a transaction-enabling system, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a production facility including a core task,wherein the core task includes a production task; a controller,including: a facility description circuit structured to interpret aplurality of historical facility parameter values and a correspondingplurality of historical facility outcome values; a facility predictioncircuit structured to operate an adaptive learning system, wherein theadaptive learning system is configured to train a facility productionpredictor in response to the plurality of historical facility parametervalues and the corresponding plurality of historical facility outcomevalues; wherein the facility description circuit is further structuredto interpret a plurality of present state facility parameter values; andwherein the facility prediction circuit is further structured to operatethe adaptive learning system to predict a present state facility outcomevalue in response to the plurality of present state facility parametervalues.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the present state facilityoutcome value includes a facility production outcome.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the present state facilityoutcome value includes a facility production outcome probabilitydistribution.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the present state facilityoutcome value includes at least one value selected from the valuesconsisting of: a production volume description of the production task; aproduction quality description of the production task; a facilityresource utilization description; an input resource utilizationdescription; and a production timing description of the production task.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the facility descriptioncircuit is further structured to interpret historical external data fromat least one external data source, and wherein the adaptive learningsystem is further configured to train the facility production predictorin response to the historical external data.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the at least one external datasource includes at least one data source selected from the data sourcesconsisting of: a social media data source; a behavioral data source; aspot market price for an energy source; and a forward market price foran energy source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the facility descriptioncircuit is further structured to interpret present external data fromthe at least one external data source, and wherein the adaptive learningsystem is further configured to predict the present state facilityoutcome value in response to the present external data.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includeinterpreting a plurality of historical facility parameter values and acorresponding plurality of historical facility outcome values; operatingan adaptive learning system, thereby training a facility productionpredictor in response to the plurality of historical facility parametervalues and the corresponding plurality of historical facility outcomevalues; interpreting a plurality of present state facility parametervalues; and operating the adaptive learning system to predict a presentstate facility outcome value in response to the plurality of presentstate facility parameter values.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the present state facilityoutcome value includes a facility production outcome.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the present state facilityoutcome value includes a facility production outcome probabilitydistribution.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the present state facilityoutcome value includes at least one value selected from the valuesconsisting of: a production volume description of a production task; aproduction quality description of a production task; a facility resourceutilization description; an input resource utilization description; anda production timing description of a production task.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include interpreting historical external datafrom at least one external data source, and operating the adaptivelearning system to further train the facility production predictor inresponse to the historical external data.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the at least one external datasource includes at least one data source selected from the data sourcesconsisting of: a social media data source; a behavioral data source; aspot market price for an energy source; and a forward market price foran energy source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include interpreting present external data fromthe at least one external data source, and operating the adaptivelearning system to predict the present state facility outcome valuefurther in response to the present external data.

The present disclosure describes a transaction-enabling system, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a facility including a core task; a controller,including: a facility description circuit structured to interpret aplurality of historical facility parameter values and a correspondingplurality of historical facility outcome values; a facility predictioncircuit structured to operate an adaptive learning system, wherein theadaptive learning system is configured to train a facility resourceallocation circuit in response to the plurality of historical facilityparameter values and the corresponding plurality of historical facilityoutcome values; wherein the facility description circuit is furtherstructured to interpret a plurality of present state facility parametervalues; and wherein the trained facility resource allocation circuit isfurther structured to adjust, in response to the plurality of presentstate facility parameter values, a plurality of facility resourcevalues.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the plurality of facilityresource values include: a provisioning and an allocation of facilityenergy resources; and a provisioning and an allocation of facilitycompute resources.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the trained facility resourceallocation circuit is further structured to adjust the plurality offacility resource values by one of producing or selecting a favorablefacility resource utilization profile from among a set of availablefacility resource utilization profiles.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the trained facility resourceallocation circuit is further structured to adjust the plurality offacility resource values by one of producing or selecting a favorablefacility resource output selection from among a set of availablefacility resource output values.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the trained facility resourceallocation circuit is further structured to adjust the plurality offacility resource values by one of producing or selecting a favorablefacility resource input profile from among a set of available facilityresource input profiles.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the trained facility resourceallocation circuit is further structured to adjust the plurality offacility resource values by one of producing or selecting a favorablefacility resource configuration profile from among a set of availablefacility resource configuration profiles.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the facility descriptioncircuit is further structured to interpret historical external data fromat least one external data source, and wherein the adaptive learningsystem is further configured to train the facility resource allocationcircuit in response to the historical external data.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the at least one external datasource includes at least one data source selected from the data sourcesconsisting of: a social media data source; a behavioral data source; aspot market price for an energy source; and a forward market price foran energy source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the facility descriptioncircuit is further structured to interpret present external data fromthe at least one external data source, and wherein the trained facilityresource allocation circuit is further structured to adjust theplurality of facility resource values in response to the presentexternal data.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includeinterpreting a plurality of historical facility parameter values and acorresponding plurality of historical facility outcome values; operatingan adaptive learning system, thereby training a facility resourceallocation circuit in response to the plurality of historical facilityparameter values and the corresponding plurality of historical facilityoutcome values; interpreting a plurality of present state facilityparameter values; and adjusting, in response to the plurality of presentstate facility parameter values, a plurality of facility resourcevalues.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the plurality of facilityresource values include: a provisioning and an allocation of facilityenergy resources; and a provisioning and an allocation of facilitycompute resources.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include adjusting the plurality of facilityresource values by selecting a favorable facility resource utilizationprofile from among a set of available facility resource utilizationprofiles.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include adjusting the plurality of facilityresource values by producing a favorable facility resource utilizationprofile relative to a set of available facility resource utilizationprofiles.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include updating the set of available facilityresource utilization profiles in response to the plurality of facilityresource values.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include adjusting the plurality of facilityresource values by selecting a favorable facility resource outputselection from among a set of available facility resource output values.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include adjusting the plurality of facilityresource values by producing a facility resource output selectionrelative to a set of available facility resource output values.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include updating the set of available facilityresource output values in response to the plurality of facility resourcevalues.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include adjusting the plurality of facilityresource values by selecting a favorable facility resource input profilefrom among a set of available facility resource input profiles.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include adjusting the plurality of facilityresource values by producing a facility resource input profile relativeto a set of available facility resource input profiles.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include updating the set of available facilityresource input profiles in response to the plurality of facilityresource values.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include adjusting the plurality of facilityresource values by selecting a favorable facility resource configurationprofile from among a set of available facility resource configurationprofiles.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include adjusting the plurality of facilityresource values by producing a facility resource configuration profilerelative to a set of available facility resource configuration profiles.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include updating the set of available facilityresource configuration profiles in response to the plurality of facilityresource values.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include interpreting historical external datafrom at least one external data source, and operating the adaptivelearning system to further train the facility resource allocationcircuit in response to the historical external data.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the at least one external datasource includes at least one data source selected from the data sourcesconsisting of: a social media data source; a behavioral data source; aspot market price for an energy source; and a forward market price foran energy source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include interpreting present external data fromthe at least one external data source, and further adjusting theplurality of facility resource values in response to the presentexternal data.

The present disclosure describes a transaction-enabling system, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a facility including a core task, wherein thecore task includes a customer relevant output; a controller, including:a facility description circuit structured to interpret a plurality ofhistorical facility parameter values and a corresponding plurality ofhistorical facility outcome values; a facility prediction circuitstructured to operate an adaptive learning system, wherein the adaptivelearning system is configured to train a facility production predictorin response to the plurality of historical facility parameter values andthe corresponding plurality of historical facility outcome values;wherein the facility description circuit is further structured tointerpret a plurality of present state facility parameter values;wherein the trained facility production predictor is configured todetermine a customer contact indicator in response to the plurality ofpresent state facility parameter values; and a customer notificationcircuit structured to provide a notification to a customer in responseto the customer contact indicator.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the customer includes one of acurrent customer and a prospective customer.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein determining the customercontact indicator includes performing at least one operation selectedfrom the operations consisting of: determining whether the customerrelevant output will meet a volume request from the customer;determining whether the customer relevant output will meet a qualityrequest from the customer; determining whether the customer relevantoutput will meet a timing request from the customer; and determiningwhether the customer relevant output will meet an optional request fromthe customer.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the facility descriptioncircuit is further structured to interpret historical external data fromat least one external data source, and wherein the adaptive learningsystem is further configured to train the facility production predictorin response to the historical external data.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the at least one external datasource includes at least one data source selected from the data sourcesconsisting of: a social media data source; a behavioral data source; aspot market price for an energy source; and a forward market price foran energy source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the facility descriptioncircuit is further structured to interpret present external data fromthe at least one external data source, and wherein the trained facilityproduction predictor is further configured to determine the customercontact indicator in response to the present external data.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includeinterpreting a plurality of historical facility parameter values and acorresponding plurality of historical facility outcome values; operatingan adaptive learning system, thereby training a facility productionpredictor in response to the plurality of historical facility parametervalues and the corresponding plurality of historical facility outcomevalues; interpreting a plurality of present state facility parametervalues; operating the trained facility production predictor to determinea customer contact indicator in response to the plurality of presentstate facility parameter values; and providing a notification to acustomer in response to the customer contact indicator.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the customer includes one of acurrent customer and a prospective customer.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein determining the customercontact indicator includes determining whether a customer relevantoutput will meet a volume request from the customer.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein determining the customercontact indicator includes determining whether a customer relevantoutput will meet a quality request from the customer.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein determining the customercontact indicator determining whether a customer relevant output willmeet a timing request from the customer.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein determining the customercontact indicator includes determining whether a customer relevantoutput will meet an optional request from the customer.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include including interpreting historicalexternal data from at least one external data source, and operating theadaptive learning system to further train the facility productionpredictor in response to the historical external data.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the at least one external datasource includes at least one data source selected from the data sourcesconsisting of: a social media data source; a behavioral data source; aspot market price for an energy source; and a forward market price foran energy source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include interpreting present external data fromthe at least one external data source, and operating the trainedfacility production predictor to further determine the customer contactindicator in response to the present external data.

The present disclosure describes a transaction-enabling system, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a facility including a core task, wherein thecore task includes a customer relevant output; a controller, including:a facility description circuit structured to interpret a plurality ofhistorical facility parameter values and a corresponding plurality ofhistorical facility outcome values; a facility prediction circuitstructured to operate an adaptive learning system, wherein the adaptivelearning system is configured to train a facility production predictorin response to the plurality of historical facility parameter values andthe corresponding plurality of historical facility outcome values;wherein the facility description circuit is further structured tointerpret a plurality of present state facility parameter values;wherein the trained facility production predictor is configured todetermine a customer contact indicator in response to the plurality ofpresent state facility parameter values; and a customer notificationcircuit structured to provide a notification to a customer in responseto the customer contact indicator.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the customer includes one of acurrent customer and a prospective customer.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein determining the customercontact indicator includes performing at least one operation selectedfrom the operations consisting of: determining whether the customerrelevant output will meet a volume request from the customer;determining whether the customer relevant output will meet a qualityrequest from the customer; determining whether the customer relevantoutput will meet a timing request from the customer; and determiningwhether the customer relevant output will meet an optional request fromthe customer.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the facility descriptioncircuit is further structured to interpret historical external data fromat least one external data source, and wherein the adaptive learningsystem is further configured to train the facility production predictorin response to the historical external data.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the at least one external datasource includes at least one data source selected from the data sourcesconsisting of: a social media data source; a behavioral data source; aspot market price for an energy source; and a forward market price foran energy source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the facility descriptioncircuit is further structured to interpret present external data fromthe at least one external data source, and wherein the trained facilityproduction predictor is further configured to determine the customercontact indicator in response to the present external data.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includeinterpreting a plurality of historical facility parameter values and acorresponding plurality of historical facility outcome values; operatingan adaptive learning system, thereby training a facility productionpredictor in response to the plurality of historical facility parametervalues and the corresponding plurality of historical facility outcomevalues; interpreting a plurality of present state facility parametervalues; operating the trained facility production predictor to determinea customer contact indicator in response to the plurality of presentstate facility parameter values; and providing a notification to acustomer in response to the customer contact indicator.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the customer includes one of acurrent customer and a prospective customer.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein determining the customercontact indicator includes determining whether a customer relevantoutput will meet a volume request from the customer.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein determining the customercontact indicator includes determining whether a customer relevantoutput will meet a quality request from the customer.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein determining the customercontact indicator determining whether a customer relevant output willmeet a timing request from the customer.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein determining the customercontact indicator includes determining whether a customer relevantoutput will meet an optional request from the customer.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include including interpreting historicalexternal data from at least one external data source, and operating theadaptive learning system to further train the facility productionpredictor in response to the historical external data.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the at least one external datasource includes at least one data source selected from the data sourcesconsisting of: a social media data source; a behavioral data source; aspot market price for an energy source; and a forward market price foran energy source.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include interpreting present external data fromthe at least one external data source, and operating the trainedfacility production predictor to further determine the customer contactindicator in response to the present external data.

The present disclosure describes a transaction-enabling system, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include an energy and compute facility including: atleast one of a compute task or a compute resource; and at least one ofan energy source or an energy utilization requirement; and a controller,including: a facility description circuit structured to interpretdetected conditions, wherein the detected conditions include at leastone condition selected from the conditions consisting of: an inputresource for the facility; a facility resource; an output parameter forthe facility; and an external condition related to an output of thefacility; and a facility configuration circuit structured to operate anadaptive learning system, wherein the adaptive learning system isconfigured to adjust a facility configuration based on the detectedconditions.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adaptive learning systemincludes at least one of a machine learning system and an artificialintelligence (AI) system.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein adjusting the facilityconfiguration further includes at least one operation selected from theoperations consisting of: performing a purchase or sale transaction onone of an energy spot market or an energy forward market; performing apurchase or sale transaction on one of a compute resource spot market ora compute resource forward market; and performing a purchase or saletransaction on one of an energy credit spot market or an energy creditforward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the facility further includesa networking task, and wherein adjusting the facility configurationfurther includes at least one operation selected from the operationsconsisting of: performing a purchase or sale transaction on one of anetwork bandwidth spot market or a network bandwidth forward market; andperforming a purchase or sale transaction on one of a spectrum spotmarket or a spectrum forward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting the facilityconfiguration further includes adjusting at least one task of thefacility to reduce the energy utilization requirement.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includeinterpreting detected conditions relative to a facility, wherein thedetected conditions include at least one condition selected from theconditions consisting of: an input resource for the facility; a facilityresource; an output parameter for the facility; and an externalcondition related to an output of the facility; and operating anadaptive learning system, thereby adjusting a facility configurationbased on the detected conditions.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein adjusting the facilityconfiguration further includes performing a purchase or sale transactionon one of an energy spot market or an energy forward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein adjusting the facilityconfiguration further includes performing a purchase or sale transactionon one of a compute resource spot market or a compute resource forwardmarket.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein adjusting the facilityconfiguration further includes performing a purchase or sale transactionon one of an energy credit spot market or an energy credit forwardmarket.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein adjusting the facilityconfiguration further includes performing a purchase or sale transactionon one of a network bandwidth spot market or a network bandwidth forwardmarket.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein adjusting the facilityconfiguration further includes performing a purchase or sale transactionon one of a spectrum spot market or a spectrum forward market.

The present disclosure describes a transaction-enabling system, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include an energy and compute facility including: atleast one of a compute task or a compute resource; and at least one ofan energy source or an energy utilization requirement; and a controller,including: a facility description circuit structured to interpretdetected conditions, wherein the detected conditions relate to a set ofinput resources for the facility; and a facility configuration circuitstructured to operate an adaptive learning system, wherein the adaptivelearning system is configured to adjust a facility configuration basedon the detected conditions.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adaptive learning systemincludes at least one of a machine learning system and an artificialintelligence (AI) system.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein adjusting the facilityconfiguration further includes at least one operation selected from theoperations consisting of: performing a purchase or sale transaction onone of an energy spot market or an energy forward market; performing apurchase or sale transaction on one of a compute resource spot market ora compute resource forward market; and performing a purchase or saletransaction on one of an energy credit spot market or an energy creditforward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the facility further includesa networking task, and wherein adjusting the facility configurationfurther includes at least one operation selected from the operationsconsisting of: performing a purchase or sale transaction on one of anetwork bandwidth spot market or a network bandwidth forward market; andperforming a purchase or sale transaction on one of a spectrum spotmarket or a spectrum forward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting the facilityconfiguration further includes adjusting at least one task orconfiguration of a resource of the facility to change an input resourcerequirement for the facility.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includeinterpreting detected conditions relative to a facility, wherein thedetected conditions relate to a set of input resources for the facility;and operating an adaptive learning system, thereby adjusting a facilityconfiguration based on the detected conditions.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting includesperforming a purchase or sale transaction on one of an energy spotmarket or an energy forward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting includesperforming a purchase or sale transaction on one of a compute resourcespot market or a compute resource forward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting includesperforming a purchase or sale transaction on one of an energy creditspot market or an energy credit forward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting includesperforming a purchase or sale transaction on one of a network bandwidthspot market or a network bandwidth forward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting includesperforming a purchase or sale transaction on one of a spectrum spotmarket or a spectrum forward market.

The present disclosure describes a transaction-enabling system, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include an energy and compute facility including: atleast one of a compute task or a compute resource; and at least one ofan energy source or an energy utilization requirement; and a controller,including: a facility description circuit structured to interpretdetected conditions, wherein the detected conditions relate to at leastone resource of the facility; and a facility configuration circuitstructured to operate an adaptive learning system, wherein the adaptivelearning system is configured to adjust a facility configuration basedon the detected conditions.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adaptive learning systemincludes at least one of a machine learning system and an artificialintelligence (AI) system.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein adjusting the facilityconfiguration further includes at least one operation selected from theoperations consisting of: performing a purchase or sale transaction onone of an energy spot market or an energy forward market; performing apurchase or sale transaction on one of a compute resource spot market ora compute resource forward market; and performing a purchase or saletransaction on one of an energy credit spot market or an energy creditforward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the facility further includesa networking task, and wherein adjusting the facility configurationfurther includes at least one operation selected from the operationsconsisting of: performing a purchase or sale transaction on one of anetwork bandwidth spot market or a network bandwidth forward market; andperforming a purchase or sale transaction on one of a spectrum spotmarket or a spectrum forward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the facility further includesat least one additional facility resource; wherein the adjusting thefacility configuration further includes adjusting a utilization of thecompute resource and the at least one additional facility resource; andwherein the at least one additional facility resource includes at leastone of a network resource, a data storage resource, or a spectrumresource.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includeinterpreting detected conditions relative to a facility, wherein thedetected conditions relate to at least one resource of the facility; andoperating an adaptive learning system, thereby adjusting a facilityconfiguration based on the detected conditions.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting the facilityconfiguration includes performing a purchase or sale transaction on oneof an energy spot market or an energy forward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting the facilityconfiguration includes performing a purchase or sale transaction on oneof a spectrum spot market or a spectrum forward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting the facilityconfiguration includes performing a purchase or sale transaction on oneof a compute resource spot market or a compute resource forward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting the facilityconfiguration includes performing a purchase or sale transaction on oneof an energy credit spot market or an energy credit forward market.

The present disclosure describes a transaction-enabling system, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include an energy and compute facility including: atleast one of a compute task or a compute resource; and at least one ofan energy source or an energy utilization requirement; and a controller,including: a facility description circuit structured to interpretdetected conditions, wherein the detected conditions include an outputparameter for the facility; and a facility configuration circuitstructured to operate an adaptive learning system, wherein the adaptivelearning system is configured to adjust a facility configuration basedon the detected conditions.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adaptive learning systemincludes at least one of a machine learning system and an artificialintelligence (AI) system.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein adjusting the facilityconfiguration further includes at least one operation selected from theoperations consisting of: performing a purchase or sale transaction onone of an energy spot market or an energy forward market; performing apurchase or sale transaction on one of a compute resource spot market ora compute resource forward market; and performing a purchase or saletransaction on one of an energy credit spot market or an energy creditforward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the facility further includesa networking task, and wherein adjusting the facility configurationfurther includes at least one operation selected from the operationsconsisting of: performing a purchase or sale transaction on one of anetwork bandwidth spot market or a network bandwidth forward market; andperforming a purchase or sale transaction on one of a spectrum spotmarket or a spectrum forward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting the facilityconfiguration further includes adjust one task of the facility toprovide at least one of: an increased facility output volume, anincreased facility quality value, or an adjusted facility output timevalue.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includeinterpreting detected conditions relative to a facility, wherein thedetected conditions include an output parameter for the facility; andoperating an adaptive learning system thereby adjusting a facilityconfiguration based on the detected conditions.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting the facilityconfiguration includes performing a purchase or sale transaction on oneof an energy spot market or an energy forward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting the facilityconfiguration includes performing a purchase or sale transaction on oneof a spectrum spot market or a spectrum forward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting the facilityconfiguration includes performing a purchase or sale transaction on oneof a compute resource spot market or a compute resource forward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting the facilityconfiguration includes performing a purchase or sale transaction on oneof an energy credit spot market or an energy credit forward market.

The present disclosure describes a transaction-enabling system, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include an energy and compute facility including: atleast one of a compute task or a compute resource; and at least one ofan energy source or an energy utilization requirement; and a controller,including: a facility description circuit structured to interpretdetected conditions, wherein the detected conditions include autilization parameter for an output of the facility; and a facilityconfiguration circuit structured to operate an adaptive learning system,wherein the adaptive learning system is configured to adjust a facilityconfiguration based on the detected conditions.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adaptive learning systemincludes at least one of a machine learning system and an artificialintelligence (AI) system.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein adjusting the facilityconfiguration further includes at least one operation selected from theoperations consisting of: performing a purchase or sale transaction onone of an energy spot market or an energy forward market; performing apurchase or sale transaction on one of a compute resource spot market ora compute resource forward market; and performing a purchase or saletransaction on one of an energy credit spot market or an energy creditforward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the facility further includesa networking task, and wherein adjusting the facility configurationfurther includes at least one operation selected from the operationsconsisting of: performing a purchase or sale transaction on one of anetwork bandwidth spot market or a network bandwidth forward market; andperforming a purchase or sale transaction on one of a spectrum spotmarket or a spectrum forward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting the facilityconfiguration further includes adjusting at least one task of thefacility to reduce the utilization parameter for the facility.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includeinterpreting detected conditions relative to a facility, wherein thedetected conditions include a utilization parameter for an output of thefacility; and operating an adaptive learning system, thereby adjusting afacility configuration based on the detected conditions.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting the facilityconfiguration includes performing a purchase or sale transaction on oneof an energy spot market or an energy forward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting the facilityconfiguration includes performing a purchase or sale transaction on oneof a spectrum spot market or a spectrum forward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting the facilityconfiguration includes performing a purchase or sale transaction on oneof a compute resource spot market or a compute resource forward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting the facilityconfiguration includes performing a purchase or sale transaction on oneof an energy credit spot market or an energy credit forward market.

The present disclosure describes a transaction-enabling system, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include an energy and compute facility including: atleast one of a compute task or a compute resource; and at least one ofan energy source or an energy utilization requirement; and a controller,including: a facility model circuit structured to operate a digital twinfor the facility; a facility description circuit structured to interpreta set of parameters from the digital twin for the facility; and afacility configuration circuit structured to operate an adaptivelearning system, wherein the adaptive learning system is configured toadjust a facility configuration based on the set of parameters from thedigital twin for the facility.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adaptive learning systemincludes at least one of a machine learning system and an artificialintelligence (AI) system.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein adjusting the facilityconfiguration further includes at least one operation selected from theoperations consisting of: performing a purchase or sale transaction onone of an energy spot market or an energy forward market; performing apurchase or sale transaction on one of a compute resource spot market ora compute resource forward market; and performing a purchase or saletransaction on one of an energy credit spot market or an energy creditforward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the facility further includesa networking task, and wherein adjusting the facility configurationfurther includes at least one operation selected from the operationsconsisting of: performing a purchase or sale transaction on one of anetwork bandwidth spot market or a network bandwidth forward market; andperforming a purchase or sale transaction on one of a spectrum spotmarket or a spectrum forward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the facility descriptioncircuit is further structured to interpret detected conditions, whereinthe detected conditions include at least one condition selected from theconditions consisting of: an input resource for the facility; a facilityresource; an output parameter for the facility; and an externalcondition related to an output of the facility; and wherein the facilitymodel circuit is further structured to update the digital twin for thefacility in response to the detected conditions.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includeoperating a model including a digital twin for a facility interpreting aset of parameters from the digital twin for the facility operating anadaptive learning system, thereby adjusting a facility configurationbased on the set of parameters from the digital twin for the facility.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting the facilityconfiguration includes performing a purchase or sale transaction on oneof an energy spot market or an energy forward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting the facilityconfiguration includes performing a purchase or sale transaction on oneof a spectrum spot market or a spectrum forward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting the facilityconfiguration includes performing a purchase or sale transaction on oneof a compute resource spot market or a compute resource forward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting the facilityconfiguration includes performing a purchase or sale transaction on oneof an energy credit spot market or an energy credit forward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting the facilityconfiguration includes performing a purchase or sale transaction on oneof a network bandwidth spot market or a network bandwidth forwardmarket.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include interpreting detected conditions relativeto the facility, wherein the detected conditions include at least onecondition selected from the conditions consisting of: an input resourcefor the facility; a facility resource; an output parameter for thefacility; and an external condition related to an output of thefacility; and operating the adaptive learning system, thereby updatingthe digital twin for the facility in response to the detectedconditions.

The present disclosure describes a transaction-enabling system, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a machine having an associated regenerativeenergy facility, the machine having a requirement for at least one of acompute task, a networking task, and an energy consumption task; and acontroller, comprising: an energy requirement circuit structured todetermine an amount of energy for the machine to service the at leastone of the compute task, the networking task, and the energy consumptiontask in response to the requirement for the at least one of the computetask, the networking task, and the energy consumption task; and anenergy distribution circuit structured to adaptively improve an energydelivery of energy produced by the associated regenerative energyfacility between the at least one of the compute task, the networkingtask, and the energy consumption task.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the energy consumption taskcomprises a core task.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the controller furthercomprises an energy market circuit structured to access an energymarket, and wherein the energy distribution circuit is furtherstructured to adaptively improve the energy delivery of the energyproduced by the associated regenerative energy facility between thecompute task, the networking task, the energy consumption task, and asale of the energy produced on the energy market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the energy market comprises atleast one of a spot market or a forward market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the energy distributioncircuit further comprises at least one of a machine learning component,an artificial intelligence component, or a neural network component.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includedetermining an amount of energy for a machine to service at least one ofa compute task, a networking task, or an energy consumption task inresponse to a compute task requirement, a networking task requirement,and an energy consumption task requirement; adaptively improving anenergy delivery between: the compute task, the networking task, and theenergy consumption task; wherein the energy delivery is of energyproduced by a regenerative energy facility of the machine.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include accessing an energy market, andadaptively improving the energy delivery of the energy produced by theregenerative energy facility between: the compute task, the networkingtask, the energy consumption task, and a sale of the energy produced onthe energy market.

The present disclosure describes a transaction-enabling system, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a machine having at least one of a compute taskrequirement, a networking task requirement, and an energy consumptiontask requirement; and a controller, comprising: a resource requirementcircuit structured to determine an amount of a resource for the machineto service at least one of the compute task requirement, the networkingtask requirement, and the energy consumption task requirement; a forwardresource market circuit structured to access a forward resource market;and a resource distribution circuit structured to execute a transactionof the resource on the forward resource market in response to thedetermined amount of the resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises acompute resource, and wherein the forward resource market comprises aforward market for compute resources.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises aspectrum allocation resource, and wherein the forward resource marketcomprises a forward market for spectrum allocation.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy credit resource, and wherein the forward resource marketcomprises a forward market for energy credits.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy resource, and wherein the forward resource market comprises aforward market for energy.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises a datastorage resource, and wherein the forward resource market comprises aforward market for data storage capacity.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy storage resource, and wherein the forward resource marketcomprises a forward market for energy storage capacity.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anetwork bandwidth resource, and wherein the forward resource marketcomprises a forward market for network bandwidth.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the transaction of theresource on the forward resource market comprises one of buying orselling the resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit is further structured to adaptively improve one of an outputvalue of the machine or a cost of operation of the machine usingexecuted transactions on the forward resource market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit further comprises at least one of a machine learning component,an artificial intelligence component, or a neural network component.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includedetermining an amount of a resource for a machine to service at leastone of a compute task requirement, a networking task requirement, and anenergy consumption task requirement of a machine; accessing a forwardresource market; and executing a transaction of the resource on theforward resource market in response to the determined amount of theresource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises acompute resource, and wherein the forward resource market comprises aforward market for compute resources.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises aspectrum allocation resource, and wherein the forward resource marketcomprises a forward market for spectrum allocation.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy credit resource, and wherein the forward resource marketcomprises a forward market for energy credits.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy resource, and wherein the forward resource market comprises aforward market for energy.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises a datastorage resource, and wherein the forward resource market comprises aforward market for data storage capacity.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy storage resource, and wherein the forward resource marketcomprises a forward market for energy storage capacity.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anetwork bandwidth resource, and wherein the forward resource marketcomprises a forward market for network bandwidth.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein executing the transaction ofthe resource on the forward resource market comprises one of buying orselling the resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include adaptively improving improve one of anoutput value of the machine or a cost of operation of the machine usingexecuted transactions on the forward resource market.

The present disclosure describes a transaction-enabling system, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a fleet of machines each having at least one of acompute task requirement, a networking task requirement, and an energyconsumption task requirement; and a controller, comprising: a resourcerequirement circuit structured to determine an amount of a resource foreach of the machines to service at least one of the compute taskrequirement, the networking task requirement, and the energy consumptiontask requirement for each corresponding machine; a forward resourcemarket circuit structured to access a forward resource market; and aresource distribution circuit structured to execute an aggregatedtransaction of the resource on the forward resource market in responseto the determined amount of the resource for each of the machines.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises acompute resource, and wherein the forward resource market comprises aforward market for compute resources.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises aspectrum allocation resource, and wherein the forward resource marketcomprises a forward market for spectrum allocation.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy credit resource, and wherein the forward resource marketcomprises a forward market for energy credits.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy resource, and wherein the forward resource market comprises aforward market for energy.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises a datastorage resource, and wherein the forward resource market comprises aforward market for data storage capacity.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy storage resource, and wherein the forward resource marketcomprises a forward market for energy storage capacity.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anetwork bandwidth resource, and wherein the forward resource marketcomprises a forward market for network bandwidth.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the aggregated transaction ofthe resource on the forward resource market comprises one of buying orselling the resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit is further structured to adaptively improve one of an aggregateoutput value of the fleet of machines or a cost of operation of thefleet of machines using executed aggregated transactions on the forwardresource market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit further comprises at least one of a machine learning component,an artificial intelligence component, or a neural network component.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includedetermining an amount of a resource, for each of machine of a fleet ofmachines, to service at least one of a compute task requirement, anetworking task requirement, and an energy consumption task requirementfor each corresponding machine; accessing a forward resource marketexecuting an aggregated transaction of the resource on the forwardresource market in response to the determined amount of the resource foreach of the machines.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises acompute resource, and wherein the forward resource market comprises aforward market for compute resources.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises aspectrum allocation resource, and wherein the forward resource marketcomprises a forward market for spectrum allocation.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy credit resource, and wherein the forward resource marketcomprises a forward market for energy credits.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy resource, and wherein the forward resource market comprises aforward market for energy.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises a datastorage resource, and wherein the forward resource market comprises aforward market for data storage capacity.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy storage resource, and wherein the forward resource marketcomprises a forward market for energy storage capacity.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anetwork bandwidth resource, and wherein the forward resource marketcomprises a forward market for network bandwidth.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein executing the aggregatedtransaction of the resource on the forward resource market comprises oneof buying or selling the resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include adaptively improving one of an aggregateoutput value of the fleet of machines or a cost of operation of thefleet of machines using executed aggregated transactions on the forwardresource market.

The present disclosure describes a transaction-enabling system, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a fleet of machines each having a requirement forat least one of a compute task, a networking task, and an energyconsumption task; and a controller, comprising: a resource requirementcircuit structured to determine an amount of a resource for each of themachines to service the requirement for the at least one of the computetask, the networking task, and the energy consumption task for eachcorresponding machine; and a resource distribution circuit structured toadaptively improve a resource utilization of the resource for each ofthe machines between the compute task, the networking task, and theenergy consumption task for each corresponding machine.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises acompute resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises aspectrum allocation resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy credit resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises a datastorage resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy storage resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anetwork bandwidth resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit further comprises at least one of a machine learning component,an artificial intelligence component, or a neural network component.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includedetermining an amount of a resource, for each of machine of a fleet ofmachines, to service a requirement of at least one of a compute task, anetworking task, and an energy consumption task for each correspondingmachine; and adaptively improving a resource utilization of the resourcefor each of the machines between the compute task, the networking task,and the energy consumption task for each corresponding machine.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises acompute resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises aspectrum allocation resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy credit resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises a datastorage resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy storage resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anetwork bandwidth resource.

The present disclosure describes a transaction-enabling system, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a machine having at least one of a compute taskrequirement, a networking task requirement, and an energy consumptiontask requirement; and a controller, comprising: a resource requirementcircuit structured to determine an amount of a resource for the machineto service at least one of the compute task requirement, the networkingtask requirement, and the energy consumption task requirement; aresource market circuit structured to access a resource market; and aresource distribution circuit structured to execute a transaction of theresource on the resource market in response to the determined amount ofthe resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy resource, and wherein the resource market comprises a spot marketfor energy.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy credit resource, and wherein the resource market comprises a spotmarket for energy credits.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises aspectrum allocation resource, and wherein the resource market comprisesa spot market for spectrum allocation.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit is further structured to adaptively improve one of an outputvalue of the machine or a cost of operation of the machine usingexecuted transactions on the resource market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit further comprises at least one of a machine learning component,an artificial intelligence component, or a neural network component.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includedetermining an amount of a resource for a machine to service at leastone of a compute task requirement, a networking task requirement, and anenergy consumption task requirement; accessing a resource market; andexecuting a transaction of the resource on the resource market inresponse to the determined amount of the resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy resource, and wherein the resource market comprises a spot marketfor energy.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy credit resource, and wherein the resource market comprises a spotmarket for energy credits.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises aspectrum allocation resource, and wherein the resource market comprisesa spot market for spectrum allocation.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include adaptively improving one of an outputvalue of the machine or a cost of operation of the machine usingexecuted transactions on the resource market.

The present disclosure describes a transaction-enabling system, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a fleet of machines each having at least one of acompute task requirement, a networking task requirement, and an energyconsumption task requirement; and a controller, comprising: a resourcerequirement circuit structured to determine an amount of a resource foreach of the machines to service at least one of the compute taskrequirement, the networking task requirement, and the energy consumptiontask requirement for each corresponding machine; a resource marketcircuit structured to access a resource market; and a resourcedistribution circuit structured to execute an aggregated transaction ofthe resource on the resource market in response to the determined amountof the resource for each of the machines.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy resource, and wherein the resource market comprises a spot marketfor energy.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy credit resource, and wherein the resource market comprises a spotmarket for energy credits.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises aspectrum allocation resource, and wherein the resource market comprisesa spot market for spectrum allocation.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit is further structured to adaptively improve one of an aggregateoutput value of the fleet of machines or a cost of operation of thefleet of machines using executed transactions on the resource market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit further comprises at least one of a machine learning component,an artificial intelligence component, or a neural network component.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includedetermining an amount of a resource, for each of machine of a fleet ofmachines, to service at least one of a compute task requirement, anetworking task requirement, and an energy consumption task requirementfor each corresponding machine; accessing a resource market; andexecuting an aggregated transaction of the resource on the resourcemarket in response to the determined amount of the resource for each ofthe machines.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy resource, and wherein the resource market comprises a spot marketfor energy.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy credit resource, and wherein the resource market comprises a spotmarket for energy credits.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises aspectrum allocation resource, and wherein the resource market comprisesa spot market for spectrum allocation.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include adaptively improving one of an aggregateoutput value of the fleet of machines or a cost of operation of thefleet of machines using executed transactions on the resource market.

The present disclosure describes a transaction-enabling system, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a machine having at least one of a compute taskrequirement, a networking task requirement, and an energy consumptiontask requirement; and a controller, comprising: a resource requirementcircuit structured to determine an amount of a resource for the machineto service at least one of the compute task requirement, the networkingtask requirement, and the energy consumption task requirement; a socialmedia data circuit structured to interpret data from a plurality ofsocial media data sources; a forward resource market circuit structuredto access a forward resource market; a market forecasting circuitstructured to predict a forward market price of the resource on theforward resource market in response to the plurality of social mediadata sources; and a resource distribution circuit structured to executea transaction of the resource on the forward resource market in responseto the determined amount of the resource and the predicted forwardmarket price of the resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises aspectrum allocation resource, and wherein the forward resource marketcomprises a forward market for spectrum allocation.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy credit resource, and wherein the forward resource marketcomprises a forward market for energy credits.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy resource, and wherein the forward resource market comprises aforward market for energy.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the transaction of theresource on the forward resource market comprises one of buying orselling the resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the market forecasting circuitfurther comprises at least one of a machine learning component, anartificial intelligence component, or a neural network component.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit is further structured to adaptively improve one of an outputvalue of the machine or a cost of operation of the machine usingexecuted transactions on the forward resource market.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit further comprises at least one of a machine learning component,an artificial intelligence component, or a neural network component.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includedetermining an amount of a resource for a machine to service at leastone of a compute task requirement, a networking task requirement, and anenergy consumption task requirement; interpreting data from a pluralityof social media data sources; accessing a forward resource market;predicting a forward market price of the resource on the forwardresource market in response to the plurality of social media datasources; and executing a transaction of the resource on the forwardresource market in response to the determined amount of the resource andthe predicted forward market price of the resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises aspectrum allocation resource, and wherein the forward resource marketcomprises a forward market for spectrum allocation.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy credit resource, and wherein the forward resource marketcomprises a forward market for energy credits.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy resource, and wherein the forward resource market comprises aforward market for energy.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein executing the transaction ofthe resource on the forward resource market comprises one of buying orselling the resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include adaptively improving one of an outputvalue of the machine or a cost of operation of the machine usingexecuted transactions on the forward resource market.

The present disclosure describes a transaction-enabling system, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a machine having at least one of a compute taskrequirement, a networking task requirement, and an energy consumptiontask requirement; and a controller. The controller including a resourcerequirement circuit structured to determine an amount of a resource forthe machine to service at least one of the compute task requirement, thenetworking task requirement, and the energy consumption taskrequirement; a resource market circuit structured to access a resourcemarket; a market testing circuit structured to execute a firsttransaction of the resource on the resource market in response to thedetermined amount of the resource; and an arbitrage execution circuitstructured to execute a second transaction of the resource on theresource market in response to the determined amount of the resource andfurther in response to an outcome of the execution of the firsttransaction, wherein the second transaction comprises a largertransaction than the first transaction.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises acompute resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises aspectrum allocation resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy credit resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises a datastorage resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy storage resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anetwork bandwidth resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the arbitrage executioncircuit is further structured to adaptively improve an arbitrageparameter by adjusting a relative size of the first transaction and thesecond transaction.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the arbitrage parametercomprises at least one parameter selected from the parameters consistingof: a similarity value in a market response of the first transaction andthe second transaction; a confidence value of the first transaction toprovide test information for the second transaction; and a market effectof the first transaction.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the arbitrage executioncircuit further comprises at least one of a machine learning component,an artificial intelligence component, or a neural network component.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includedetermining an amount of a resource for a machine to service at leastone of a compute task requirement, a networking task requirement, and anenergy consumption task requirement; accessing a resource market;executing a first transaction of the resource on the resource market inresponse to the determined amount of the resource; and executing asecond transaction of the resource on the resource market in response tothe determined amount of the resource and further in response to anoutcome of the execution of the first transaction, wherein the secondtransaction comprises a larger transaction than the first transaction.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises acompute resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises aspectrum allocation resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy credit resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises a datastorage resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anenergy storage resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises anetwork bandwidth resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include adaptively improving an arbitrageparameter by adjusting a relative size of the first transaction and thesecond transaction.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the arbitrage parametercomprises at least one parameter selected from the parameters consistingof: a similarity value in a market response of the first transaction andthe second transaction; a confidence value of the first transaction toprovide test information for the second transaction; and a market effectof the first transaction.

The present disclosure describes an apparatus, the apparatus accordingto one disclosed non-limiting embodiment of the present disclosure caninclude a resource requirement circuit structured to determine an amountof a resource for a machine to service at least one of a compute taskrequirement, a networking task requirement, and an energy consumptiontask requirement; a resource market circuit structured to access aresource market; a market testing circuit structured to execute a firsttransaction of the resource on the resource market in response to thedetermined amount of the resource; and an arbitrage execution circuitstructured to execute a second transaction of the resource on theresource market in response to the determined amount of the resource andfurther in response to an outcome of the execution of the firsttransaction, wherein the second transaction comprises a largertransaction than the first transaction.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource comprises atleast one resource selected from the resources consisting of: a computeresource; a spectrum allocation resources; an energy credit resource; anenergy resource; a data storage resource; an energy storage resource;and a network bandwidth resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the arbitrage executioncircuit is further structured to adaptively improve an arbitrageparameter by adjusting a relative size of the first transaction and thesecond transaction.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the arbitrage parametercomprises a similarity value in a market response of the firsttransaction and the second transaction.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the arbitrage parameterfurther comprises a market effect of the first transaction.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the arbitrage parametercomprises a confidence value of the first transaction to provide testinformation for the second transaction.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the arbitrage parameterfurther comprises a market effect of the first transaction.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the arbitrage executioncircuit further comprises at least one of a machine learning component,an artificial intelligence component, or a neural network component.

The present disclosure describes a transaction-enabling system, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a machine having an associated resource capacityfor a resource, the machine having a requirement for at least one of acore task, a compute task, an energy storage task, a data storage task,and a networking task; and a controller, comprising: a resourcerequirement circuit structured to determine an amount of the resource toservice the requirement of the at least one of the core task, thecompute task, the energy storage task, the data storage task, and thenetworking task in response to the requirement of the at least one ofthe core task, the compute task, the energy storage task, the datastorage task, and the networking task; and a resource distributioncircuit structured to adaptively improve, in response to the associatedresource capacity, a resource delivery of the resource between the coretask, the compute task, the energy storage task, the data storage task,and the networking task.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the associated resourcecapacity comprises a compute capacity for a compute resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the associated resourcecapacity comprises an energy capacity for an energy resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the associated resourcecapacity comprises a network bandwidth capacity for a networkingresource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the associated resourcecapacity comprises an energy storage capacity for an energy storageresource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit is further structured to adaptively improve the resourcedelivery in response to one of a quality and an output associated withthe core task.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit is further structured to adaptively improve the resourcedelivery in response to a cost of operation of the machine.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit further comprises at least one of a machine learning component,an artificial intelligence component, or a neural network component.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includedetermining an amount of a resource to service a core task, a computetask, an energy storage task, a data storage task, and a networking taskof a machine, in response to at least one of a core task requirement, acompute task requirement, an energy storage task requirement, a datastorage task requirement, and a networking task requirement of themachine; and adaptively improving, in response to an associated resourcecapacity of the machine, a resource delivery of the resource between thecore task, the compute task, the energy storage task, the data storagetask, and the networking task.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the associated resourcecapacity comprises a compute capacity for a compute resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the associated resourcecapacity comprises an energy capacity for an energy resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the associated resourcecapacity comprises a network bandwidth capacity for a networkingresource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the associated resourcecapacity comprises an energy storage capacity for an energy storageresource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include adaptively improving the resourcedelivery in response to one of a quality and an output associated withthe core task of the machine.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include adaptively improving the resourcedelivery in response to a cost of operation of the machine.

The present disclosure describes a transaction-enabling system, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a fleet of machines each having an associatedresource capacity for a resource, and each machine of the fleet ofmachines further having a requirement for at least one of a core task, acompute task, an energy storage task, a data storage task, and anetworking task; and a controller. The controller including a resourcerequirement circuit structured to determine an aggregated amount of theresource to service the at least one of the core task, the compute task,the energy storage task, the data storage task, and the networking taskfor each of the fleet of machines in response to the requirement of theat least one of the core task, the compute task, the energy storagetask, the data storage task, and the networking task for each one of thefleet of machines; and a resource distribution circuit structured toadaptively improve, in response to an aggregated associated resourcecapacity, an aggregated resource delivery of the resource between thecore task, the compute task, the energy storage task, the data storagetask, and the networking task for each machine of the fleet of machines.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the aggregated associatedresource capacity comprises a compute capacity for a compute resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the aggregated associatedresource capacity comprises an energy capacity for an energy resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the aggregated associatedresource capacity comprises a network bandwidth capacity for anetworking resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the aggregated associatedresource capacity comprises an energy storage capacity for an energystorage resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit is further structured to adaptively improve the aggregatedresource delivery in response to one of a quality and an outputassociated with the core task for each machine of the fleet of machines.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit is further structured to adaptively improve the aggregatedresource delivery in response to an aggregated one of a quality and anoutput associated with the core task for the fleet of machines.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit is further structured to interpret a resource transferabilityvalue between at least two machines of the fleet of machines, and toadaptively improve the aggregated resource delivery further in responseto the resource transferability value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit is further structured to adaptively improve the aggregatedresource delivery in response to a cost of operation of each machine ofthe fleet of machines.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit is further structured to adaptively improve the aggregatedresource delivery in response to an aggregated cost of operation of thefleet of machines.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit further comprises at least one of a machine learning component,an artificial intelligence component, or a neural network component.

The present disclosure describes a method, the method according to onedisclosed non-limiting embodiment of the present disclosure can includedetermining an aggregated amount of a resource to service a core task, acompute task, an energy storage task, a data storage task, and anetworking task for each machine of a fleet of machines, in response toat least one of a core task requirement, a compute task requirement, anenergy storage task requirement, a data storage task requirement, and anetworking task requirement for each machine of the fleet of machines;and adaptively improving, in response to an aggregated associatedresource capacity of the fleet of machines, an aggregated resourcedelivery of the resource between the core task, the compute task, theenergy storage task, the data storage task, and the networking task foreach machine of the fleet of machines.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include adaptively improving the aggregatedresource delivery in response to one of a quality and an outputassociated with the core task for each machine of the fleet of machines.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include adaptively improving the aggregatedresource delivery in response to an aggregated one of a quality and anoutput associated with the core task for each machine of the fleet ofmachines.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include interpreting a resource transferabilityvalue between at least two machines of the fleet of machines, andadaptively improving the aggregated resource delivery further inresponse to the resource transferability value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include adaptively improving the aggregatedresource delivery in response to a cost of operation of each machine ofthe fleet of machines.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may further include adaptively improving the aggregatedresource delivery in response to an aggregated cost of operation of thefleet of machines.

The present disclosure describes an apparatus, the apparatus accordingto one disclosed non-limiting embodiment of the present disclosure caninclude a resource requirement circuit structured to determine anaggregated amount of a resource to service a core task, a compute task,an energy storage task, a data storage task, and a networking task foreach machine of a fleet of machines in response to at least one of acore task requirement, a compute task requirement, an energy storagetask requirement, a data storage task requirement, and a networking taskrequirement for each machine of the fleet of machines; and a resourcedistribution circuit structured to adaptively improve, in response to anaggregated associated resource capacity of the fleet of machines, anaggregated resource delivery of the resource between the core task, thecompute task, the energy storage task, the data storage task, and thenetworking task for each machine of the fleet of machines.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the aggregated associatedresource capacity comprises a compute capacity for a compute resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the aggregated associatedresource capacity comprises an energy capacity for an energy resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the aggregated associatedresource capacity comprises a network bandwidth capacity for anetworking resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the aggregated associatedresource capacity comprises an energy storage capacity for an energystorage resource.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit is further structured to adaptively improve the aggregatedresource delivery in response to a quality associated with the core taskfor each machine of the fleet of machines.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit is further structured to adaptively improve the aggregatedresource delivery in response to an output associated with the core taskfor each machine of the fleet of machines.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit is further structured to adaptively improve the aggregatedresource delivery in response to an aggregated quality associated withthe core task for the fleet of machines.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit is further structured to adaptively improve the aggregatedresource delivery in response to an aggregated output associated withthe core task for the fleet of machines.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit is further structured to interpret a resource transferabilityvalue between at least two machines of the fleet of machines, and toadaptively improve the aggregated resource delivery further in responseto the resource transferability value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit is further structured to adaptively improve the aggregatedresource delivery in response to a cost of operation of each machine ofthe fleet of machines.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit is further structured to adaptively improve the aggregatedresource delivery in response to an aggregated cost of operation of thefleet of machines.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the resource distributioncircuit further comprises at least one of a machine learning component,an artificial intelligence component, or a neural network component.

Provided herein are methods and systems that improve the machines thatenable markets, including for increased efficiency, speed, reliability,and the like for participants in such markets.

Provided herein are improved machines that enable distributedtransactions to occur at scale among large numbers of participants,including human participants and automated agents.

Certain systems and operations are described herein for improving oroptimizing energy utilization and/or acquisition for compute,networking, and/or other tasks.

In embodiments, a platform for enabling transactions is provided havinga machine with a regenerative energy facility that optimizes allocationof delivery of energy produced among compute tasks, networking tasks andenergy consumption tasks.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically purchases its energy in a forward marketfor energy.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically purchases energy credits in a forwardmarket.

In embodiments, a platform for enabling transactions is provided havinga fleet of machines that automatically aggregate purchasing in a forwardmarket for energy.

In embodiments, a platform for enabling transactions is provided havinga fleet of machines that automatically aggregate purchasing energycredits in a forward market.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically purchases spectrum allocation in a forwardmarket for network spectrum.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically sells its compute capacity on a forwardmarket for compute capacity.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically sells its compute storage capacity on aforward market for storage capacity.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically sells its energy storage capacity on aforward market for energy storage capacity.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically sells its network bandwidth on a forwardmarket for network capacity.

In embodiments, a platform for enabling transactions is provided havinga fleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum.

In embodiments, a platform for enabling transactions is provided havinga fleet of machines that automatically optimize energy utilization forcompute task allocation (e.g., bitcoin mining).

In embodiments, a platform for enabling transactions is provided havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy.

In embodiments, a platform for enabling transactions is provided havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits.

In embodiments, a platform for enabling transactions is provided havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum

In embodiments, a platform for enabling transactions is provided havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market sales of compute capacity.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically purchases its energy in a spot market forenergy.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically purchases energy credits in a spot market.

In embodiments, a platform for enabling transactions is provided havinga fleet of machines that automatically aggregate purchasing in a spotmarket for energy.

In embodiments, a platform for enabling transactions is provided havinga fleet of machines that automatically aggregate purchasing energycredits in a spot market.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically purchases spectrum allocation in a spotmarket for network spectrum.

In embodiments, a platform for enabling transactions is provided havinga fleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum.

In embodiments, a platform for enabling transactions is provided havinga fleet of machines that automatically optimize energy utilization forcompute task allocation (e.g., bitcoin mining).

In embodiments, a platform for enabling transactions is provided havinga fleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy.

In embodiments, a platform for enabling transactions is provided havinga fleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy credits.

In embodiments, a platform for enabling transactions is provided havinga fleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of network spectrum.

In embodiments, a platform for enabling transactions is provided havinga fleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity.

In embodiments, a platform for enabling transactions is provided havinga fleet of machines that automatically sell their aggregate computestorage capacity on a forward market for storage capacity.

In embodiments, a platform for enabling transactions is provided havinga fleet of machines that automatically sell their aggregate energystorage capacity on a forward market for energy storage capacity.

In embodiments, a platform for enabling transactions is provided havinga fleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from social media data sources.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically forecasts forward market pricing of networkspectrum based on information collected from social media data sources.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sources.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically forecasts forward market value of computecapability based on information collected from social media datasources.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically executes an arbitrage strategy for purchaseor sale of compute capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically executes an arbitrage strategy for purchaseor sale of network spectrum or bandwidth by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy credits by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically allocates its energy capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically allocates its compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically allocates its networking capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task.

In embodiments, a platform for enabling transactions is provided havinga fleet of machines that automatically allocate collective energycapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task.

In embodiments, a platform for enabling transactions is provided havinga fleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task.

In embodiments, a platform for enabling transactions is provided havinga fleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task.

Certain systems and/or operations for utilizing a blockchain forknowledge to enable transactions are described herein.

In embodiments, a platform for enabling transactions is provided havinga smart contract wrapper using a distributed ledger wherein the smartcontract embeds IP licensing terms for intellectual property embedded inthe distributed ledger and wherein executing an operation on thedistributed ledger provides access to the intellectual property andcommits the executing party to the IP licensing terms.

In embodiments, a platform for enabling transactions is provided havinga distributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to an aggregatestack of intellectual property.

In embodiments, a platform for enabling transactions is provided havinga distributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to agree to anapportionment of royalties among the parties in the ledger.

In embodiments, a platform for enabling transactions is provided havinga distributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to an aggregatestack of intellectual property.

In embodiments, a platform for enabling transactions is provided havinga distributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to commit a party to a contract term.

In embodiments, a platform for enabling transactions is provided havinga distributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set.

In embodiments, a platform for enabling transactions is provided havinga distributed ledger that tokenizes executable algorithmic logic, suchthat operation on the distributed ledger provides provable access to theexecutable algorithmic logic.

In embodiments, a platform for enabling transactions is provided havinga distributed ledger that tokenizes a 3D printer instruction set, suchthat operation on the distributed ledger provides provable access to theinstruction set.

In embodiments, a platform for enabling transactions is provided havinga distributed ledger that tokenizes an instruction set for a coatingprocess, such that operation on the distributed ledger provides provableaccess to the instruction set.

In embodiments, a platform for enabling transactions is provided havinga distributed ledger that tokenizes an instruction set for asemiconductor fabrication process, such that operation on thedistributed ledger provides provable access to the fabrication process.

In embodiments, a platform for enabling transactions is provided havinga distributed ledger that tokenizes a firmware program, such thatoperation on the distributed ledger provides provable access to thefirmware program.

In embodiments, a platform for enabling transactions is provided havinga distributed ledger that tokenizes an instruction set for an FPGA, suchthat operation on the distributed ledger provides provable access to theFPGA.

In embodiments, a platform for enabling transactions is provided havinga distributed ledger that tokenizes serverless code logic, such thatoperation on the distributed ledger provides provable access to theserverless code logic.

In embodiments, a platform for enabling transactions is provided havinga distributed ledger that tokenizes an instruction set for a crystalfabrication system, such that operation on the distributed ledgerprovides provable access to the instruction set.

In embodiments, a platform for enabling transactions is provided havinga distributed ledger that tokenizes an instruction set for a foodpreparation process, such that operation on the distributed ledgerprovides provable access to the instruction set.

In embodiments, a platform for enabling transactions is provided havinga distributed ledger that tokenizes an instruction set for a polymerproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set.

In embodiments, a platform for enabling transactions is provided havinga distributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set.

In embodiments, a platform for enabling transactions is provided havinga distributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set.

In embodiments, a platform for enabling transactions is provided havinga distributed ledger that tokenizes a trade secret with an expertwrapper, such that operation on the distributed ledger provides provableaccess to the trade secret and the wrapper provides validation of thetrade secret by the expert.

In embodiments, a platform for enabling transactions is provided havinga distributed ledger that aggregates views of a trade secret into achain that proves which and how many parties have viewed the tradesecret.

In embodiments, a platform for enabling transactions is provided havinga distributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger.

In embodiments, a platform for enabling transactions is provided havinga distributed ledger that tokenizes an item of intellectual property anda reporting system that reports an analytic result based on theoperations performed on the distributed ledger or the intellectualproperty.

In embodiments, a platform for enabling transactions is provided havinga distributed ledger that aggregates a set of instructions, where anoperation on the distributed ledger adds at least one instruction to apre-existing set of instructions to provide a modified set ofinstructions.

Certain systems and/or operations for utilizing and/or executingtransactions with an intelligent cryptocurrency or cryptocurrencytransaction manager are described herein.

In embodiments, a platform for enabling transactions is provided havinga smart wrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location.

In embodiments, a platform for enabling transactions is provided havinga smart wrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location.

In embodiments, a platform for enabling transactions is provided havinga self-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment.

In embodiments, a platform for enabling transactions is provided havingan expert system that uses machine learning to optimize the execution ofcryptocurrency transactions based on tax status.

In embodiments, a platform for enabling transactions is provided havingan expert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information.

In embodiments, a platform for enabling transactions is provided havingan expert system that uses machine learning to optimize the execution ofa cryptocurrency transaction based on real time energy price informationfor an available energy source.

In embodiments, a platform for enabling transactions is provided havingan expert system that uses machine learning to optimize the execution ofa cryptocurrency transaction based on an understanding of availableenergy sources to power computing resources to execute the transaction.

In embodiments, a platform for enabling transactions is provided havingan expert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction.

Certain systems and operations for making forward market predictions,and/or enabling transactions utilizing forward market predictions aredescribed herein. In certain embodiments, forward market predictionsdescribed herein include non-traditional data, and/or include data forforward market predictions that are not utilized in previously knownsystems.

In embodiments, a platform for enabling transactions is provided havingan expert system that predicts a forward market price in a market basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction.

In embodiments, a platform for enabling transactions is provided havingan expert system that predicts a forward market price in a market basedon an understanding obtained by analyzing social network data sourcesand executes a transaction based on the forward market prediction.

In embodiments, a platform for enabling transactions is provided havingan expert system that predicts a forward market price in a market basedon an understanding obtained by analyzing Internet of Things datasources and executes a cryptocurrency transaction based on the forwardmarket prediction.

In embodiments, a platform for enabling transactions is provided havingan expert system that predicts a forward market price in a market basedon an understanding obtained by analyzing social network data sourcesand executes a cryptocurrency transaction based on the forward marketprediction.

In embodiments, a platform for enabling transactions is provided havingan expert system that predicts a forward market price in an energymarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction.

In embodiments, a platform for enabling transactions is provided havingan expert system that predicts a forward market price in an energymarket based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction.

In embodiments, a platform for enabling transactions is provided havingan expert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction.

In embodiments, a platform for enabling transactions is provided havingan expert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction.

In embodiments, a platform for enabling transactions is provided havingan expert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction.

In embodiments, a platform for enabling transactions is provided havingan expert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction.

In embodiments, a platform for enabling transactions is provided havingan expert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically forecasts forward market pricing of networkspectrum based on information collected from automated agent behavioraldata sources.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically forecasts forward market pricing of energycredits based on information collected from automated agent behavioraldata sources.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically forecasts forward market value of computecapability based on information collected from automated agentbehavioral data sources.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from business entity behavioraldata sources.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically forecasts forward market pricing of networkspectrum based on information collected from business entity behavioraldata sources.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically forecasts forward market pricing of energycredits based on information collected from business entity behavioraldata sources.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically forecasts forward market value of computecapability based on information collected from business entitybehavioral data sources.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from human behavioral datasources.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically forecasts forward market pricing of networkspectrum based on information collected from human behavioral datasources.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically forecasts forward market pricing of energycredits based on information collected from human behavioral datasources.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically forecasts forward market value of computecapability based on information collected from human behavioral datasources.

In embodiments, a platform for enabling transactions is provided havingan expert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction.

In embodiments, a platform for enabling transactions is provided havingan intelligent agent that is configured to solicit the attentionresources of another external intelligent agent.

In embodiments, a platform for enabling transactions is provided havinga machine that automatically purchases attention resources in a forwardmarket for attention.

In embodiments, a platform for enabling transactions is provided havinga fleet of machines that automatically aggregate purchasing in a forwardmarket for attention.

Provided herein are a flexible, intelligent energy and compute facility,as well as an intelligent energy and compute facility resourcemanagement system, including components, systems, services, modules,programs, processes and other enabling elements, such as capabilitiesfor data collection, storage and processing, automated configuration ofinputs, resources and outputs, and learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize parameters relevant to such a facility.

In embodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a likelihood of a facility production outcome.

In embodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a facility production outcome.

In embodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles.

In embodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource output selection among a set ofavailable outputs.

In embodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles.

In embodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles.

In embodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations.

In embodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility.

In embodiments, provided herein is a system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditions.relating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility.

In embodiments, provided herein is a system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of input resources.

In embodiments, provided herein is a system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of facility resources.

In embodiments, provided herein is a system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter.

In embodiments, provided herein is a system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility.

In embodiments, provided herein is a system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

An example transaction-enabling system includes a controller having: anattention market access circuit structured to interpret a number ofattention-related resources available on an attention market, anintelligent agent circuit structured to determine an attention-relatedresource acquisition value based on a cost parameter of at least one ofthe number of attention-related resources, and an attention acquisitioncircuit structured to solicit an attention-related resource in responseto the attention-related resource acquisition value.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system includes where the attention acquisition circuit isfurther structured to perform the soliciting the attention-relatedresource by performing at least one operation selected from theoperations consisting of: purchasing the attention-related resource fromthe attention market; selling the attention-related resource to theattention market; making an offer to sell the attention-related resourceto a second intelligent agent; and making an offer to purchase theattention-related resource to the second intelligent agent. An examplesystem includes where the number of attention-related resources includesat least one resource selected from the list consisting of: anadvertising placement; a search listing; a keyword listing; a banneradvertisements; a video advertisement; an embedded video advertisement;a panel activity participation; a survey activity participation; a trialactivity participation; and a pilot activity placement or participation.An example system includes one or more of: where the attention marketincludes a spot market for at least one of the number ofattention-related resources; where the cost parameter of at least one ofthe number of attention-related resources includes a future predictedcost of the at least one of the number of attention-related resources,and where the intelligent agent circuit is further structured todetermine the attention-related resource acquisition value in responseto a comparison of a first cost on the spot market with the costparameter; where the attention market includes a forward market for atleast one of the number of attention-related resources, and where thecost parameter of the at least one of the number of attention-relatedresources includes a predicted future cost; and/or where the costparameter of at least one of the number of attention-related resourcesincludes a future predicted cost of the at least one of the number ofattention-related resources, and where the intelligent agent circuit isfurther structured to determine the attention-related resourceacquisition value in response to a comparison of a first cost on theforward market with the cost parameter. An example system includes theintelligent agent circuit further structured to determine theattention-related resource acquisition value in response to the costparameter of the at least one of the number of attention-relatedresources having a value that is outside of an expected cost range forthe at least one of the number of attention-related resources. Anexample system includes the intelligent agent circuit is furtherstructured to determine the attention-related resource acquisition valuein response to a function of: the cost parameter of the at least one ofthe number of attention-related resources, and/or an effectivenessparameter of the at least one of the number of attention-relatedresources. In certain further embodiments, an example controller furtherincludes an external data circuit structured to interpret a social mediadata source, and where the intelligent agent circuit is furtherstructured to determine, in response to the social media data source, atleast one of a future predicted cost of the at least one of the numberof attention-related resources, and to utilize the future predicted costas the cost parameter and/or the effectiveness parameter of the at leastone of the number of attention-related resources.

An example system includes a fleet of machines, where each machineincludes a task system having a core task and further at least one of acompute task or a network task. The system includes a controller having:an attention market access circuit structured to interpret a number ofattention-related resources available on an attention market; anintelligent agent circuit structured to determine an attention-relatedresource acquisition value based on a cost parameter of at least one ofthe number of attention-related resources, and further based on the coretask for the corresponding machine of the fleet of machines; anattention purchase aggregating circuit structured to determine anaggregate attention-related resource purchase value in response to thenumber of attention-related resource acquisition values from eachintelligent agent circuit corresponding to each machine of the fleet ofthe machines; and an attention acquisition circuit structured topurchase an attention-related resource in response to the aggregateattention-related resource purchase value.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system includes where the attention purchase aggregating circuitis positioned at a location selected from the locations consisting of:at least partially distributed on a number of the controllerscorresponding to machines of the fleet of machines; on a selectedcontroller corresponding to one of the machines of the fleet ofmachines; and on a system controller communicatively coupled to thenumber of the controllers corresponding to machines of the fleet ofmachines. An example system includes where the attention purchaseacquisition circuit is positioned at a location selected from thelocations consisting of: at least partially distributed on a number ofthe controllers corresponding to machines of the fleet of machines; on aselected controller corresponding to one of the machines of the fleet ofmachines; and on a system controller communicatively coupled to thenumber of the controllers corresponding to machines of the fleet ofmachines.

An example procedure includes an operation to interpret a number ofattention-related resources available on an attention market, anoperation to determine an attention-related resource acquisition valuebased on a cost parameter of at least one of the number ofattention-related resources, and an operation to solicit anattention-related resource in response to the attention-related resourceacquisition value.

Certain further aspects of an example procedure are described following,any one or more of which may be present in certain embodiments. Anexample procedure further includes the operation to perform thesoliciting the attention-related resource by performing at least oneoperation selected from the operations consisting of: purchasing theattention-related resource from the attention market; selling theattention-related resource to the attention market; making an offer tosell the attention-related resource to a second intelligent agent; andmaking an offer to purchase the attention-related resource to the secondintelligent agent. An example procedure further includes where the costparameter of at least one of the number of attention-related resourcesincludes a future predicted cost of the at least one of the number ofattention-related resources, the method further including determiningthe attention-related resource acquisition value in response to acomparison of a first cost on a spot market with the cost parameter. Anexample procedure further includes an operation to interpret a socialmedia data source and an operation to determine, in response to thesocial media data source: a future predicted cost of the at least one ofthe number of attention-related resources, and to utilize the futurepredicted cost as the cost parameter; and/or an effectiveness parameterof the at least one of the number of attention-related resources. Anexample procedure further includes where the operation to determine theattention-related resource acquisition value is further based on the atleast one of the future predicted cost or the effectiveness parameter.

An example procedure includes an operation to interpret a number ofattention-related resources available on an attention market, anoperation to determine an attention-related resource acquisition valuefor each machine of a fleet of machines based on a cost parameter of atleast one of the number of attention-related resources, and furtherbased on a core task for each of a corresponding machine of the fleet ofmachines, an operation to determine an aggregate attention-relatedresource purchase value in response to the number of attention-relatedresource acquisition values corresponding to each machine of the fleetof the machines, and an operation to purchase an attention-relatedresource in response to the aggregate attention-related resourcepurchase value.

Certain further aspects of an example procedure are described following,any one or more of which may be present in certain embodiments. Anexample procedure includes where the cost parameter of at least one ofthe number of attention-related resources includes a future predictedcost of the at least one of the number of attention-related resources,and where the procedure further includes an operation to determine eachattention-related resource acquisition value in response to a comparisonof a first cost on a spot market for attention-related resources withthe cost parameter. An example procedure further includes an operationto interpret a social media data source and an operation to determine,in response to the social media data source: a future predicted cost ofthe at least one of the number of attention-related resources, and toutilize the future predicted cost as the cost parameter; and/or aneffectiveness parameter of the at least one of the number ofattention-related resources. An example procedure further includes theoperation to determine the attention-related resource acquisition valuefurther based on the future predicted cost and/or the effectivenessparameter.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a schematic diagram of components of a platform for enablingintelligent transactions in accordance with embodiments of the presentdisclosure.

FIGS. 2A-2B is a schematic diagram of additional components of aplatform for enabling intelligent transactions in accordance withembodiments of the present disclosure.

FIG. 3 is a schematic diagram of additional components of a platform forenabling intelligent transactions in accordance with embodiments of thepresent disclosure.

FIG. 4 to FIG. 31 are schematic diagrams of embodiments of neural netsystems that may connect to, be integrated in, and be accessible by theplatform for enabling intelligent transactions including ones involvingexpert systems, self-organization, machine learning, artificialintelligence and including neural net systems trained for patternrecognition, for classification of one or more parameters,characteristics, or phenomena, for support of autonomous control, andother purposes in accordance with embodiments of the present disclosure.

FIG. 32 is a schematic diagram of components of an environment includingan intelligent energy and compute facility, a host intelligent energyand compute facility resource management platform, a set of datasources, a set of expert systems, interfaces to a set of marketplatforms and external resources, and a set of user or client systemsand devices in accordance with embodiments of the present disclosure.

FIG. 33 is a schematic diagram of an energy and computing resourceplatform in accordance with embodiments of the present disclosure.

FIGS. 34 and 35 are illustrative depictions of data record schema inaccordance with embodiments of the present disclosure.

FIG. 36 is a schematic diagram of a cognitive processing system inaccordance with embodiments of the present disclosure.

FIG. 37 is a schematic flow diagram of a procedure for selecting leadsin accordance with embodiments of the present disclosure.

FIG. 38 is a schematic flow diagram of a procedure for generating a leadlist in accordance with embodiments of the present disclosure.

FIG. 39 is a schematic flow diagram of a procedure for generatingcustomized facility attributes in accordance with embodiments of thepresent disclosure.

FIG. 40 is a schematic diagram of a system including a smart contractwrapper.

FIG. 41 is a schematic flow diagram of a method for executing a smartcontract wrapper.

FIG. 42 is a schematic flow diagram of a method for updating anaggregate IP stack.

FIG. 43 is a schematic flow diagram of a method for adding assets andentities.

FIG. 44 is a schematic flow diagram of a method for updating anaggregate IP stack.

FIG. 45 is a schematic diagram of a system for providing verifiableaccess to an instruction set.

FIG. 46 is a schematic flow diagram of a method for providing verifiableaccess to an instruction set.

FIG. 47 is a schematic diagram of a system for providing verifiableaccess to algorithmic logic.

FIG. 48 is a schematic flow diagram of a method for providing verifiableaccess to executable algorithmic logic.

FIG. 49 is a schematic diagram of a system for providing verifiableaccess to firmware.

FIG. 50 is a schematic flow diagram of a method for providing verifiableaccess to firmware.

FIG. 51 is a schematic diagram of a system for providing verifiableaccess to serverless code logic.

FIG. 52 is a schematic flow diagram of a method for providing verifiableaccess to serverless code logic.

FIG. 53 is a schematic diagram of a system for providing verifiableaccess to an aggregated data set.

FIG. 54 is a schematic flow diagram of a method for providing verifiableaccess to an aggregated data set.

FIG. 55 is a schematic diagram of a system for analyzing and reportingon an aggregate stack of IP.

FIG. 56 is a schematic flow diagram of a method for analyzing andreporting on an aggregate stack of IP.

FIG. 57 is a schematic diagram of a system for improving resourceutilization for a task system.

FIG. 58 is a schematic flow diagram of a method for improving resourceutilization for a task system.

FIG. 59 is a schematic flow diagram of a method for improving resourceutilization with a substitute resource.

FIG. 60 is a schematic flow diagram of a method for improving resourceutilization with behavioral data.

FIG. 61 is a schematic flow diagram of a method for improving resourceutilization for a task system.

FIG. 62 is a schematic diagram of a system for improving acryptocurrency transaction request outcome.

FIG. 63 is a schematic flow diagram of a method for improving acryptocurrency transaction request outcome.

FIG. 64 is a schematic flow diagram of a method for improving acryptocurrency transaction request outcome.

FIG. 65 is a schematic diagram of a system for improving acryptocurrency transaction request outcome.

FIG. 66 is a schematic flow diagram of a method for improving acryptocurrency transaction request outcome.

FIG. 67 is a schematic diagram of a system for improving execution ofcryptocurrency transactions.

FIG. 68 is a schematic flow diagram of a method for improving executionof cryptocurrency transactions.

FIG. 69 is a schematic diagram of a system for improving attentionmarket transaction operations.

FIG. 70 is a schematic flow diagram of a method for improving attentionmarket transaction operations.

FIG. 71 is a schematic diagram of a system for aggregating attentionresource acquisition for a fleet.

FIG. 72 is a schematic flow diagram of a method for aggregatingattention resource acquisition for a fleet.

FIG. 73 is a schematic diagram of a system to improve productionfacility outcome predictions.

FIG. 74 is a schematic flow diagram of a method to improve productionfacility outcome predictions.

FIG. 75 is a schematic diagram of a system to improve a facilityresource parameter.

FIG. 76 is a schematic flow diagram of a method to improve a facilityresource parameter.

FIG. 77 is a schematic diagram of a system to improve a facility outputvalue.

FIG. 78 is a schematic flow diagram of a method to improve a facilityoutput value.

FIG. 79 is a schematic flow diagram of a method to improve a facilityproduction prediction.

FIG. 80 is a schematic diagram of a system to improve facility resourceutilization.

FIG. 81 is a schematic flow diagram of a method to improve facilityresource utilization.

FIG. 82 is a schematic diagram of a system to improve facility resourceoutcomes by adjusting a facility configuration.

FIG. 83 is a schematic diagram of a system for improving facilityresource outcomes using a digital twin.

FIG. 84 is a schematic flow diagram of a method for improving facilityresource outcomes using a digital twin.

FIG. 85 is a schematic diagram of a system for improving regenerativeenergy delivery for a facility.

FIG. 86 is a schematic flow diagram of a method for improvingregenerative energy delivery for a facility.

FIG. 87 is a schematic diagram of a system for improving resourceacquisition for a facility.

FIG. 88 is a schematic flow diagram of a method for improving resourceacquisition for a facility.

FIG. 89 is a schematic diagram of a system for improving resourceacquisition for a fleet of machines.

FIG. 90 is a schematic flow diagram of a method for improving resourceacquisition for a fleet of machines.

FIG. 91 is a schematic diagram of a system for improving resourceutilization for a fleet of machines.

FIG. 92 is a schematic flow diagram of a method for improving resourceutilization for a fleet of machines.

FIG. 93 is a schematic diagram of system for improving resourceutilization of a machine.

FIG. 94 is a schematic flow diagram of a method for improving resourceutilization of a machine.

FIG. 95 is a schematic diagram of a system for improving resourceutilization for a fleet of machines.

FIG. 96 is a schematic flow diagram of a method for improving resourceutilization for a fleet of machines.

FIG. 97 is a schematic diagram of a system for improving resourceutilization for a machine using social media data and a forward resourcemarket.

FIG. 98 is a schematic flow diagram of a method for improving resourceutilization for a machine using social media data and a forward resourcemarket.

FIG. 99 is a schematic diagram of a system for improving resourceutilization using an arbitrage operation.

FIG. 100 is a schematic flow diagram of a method for improving resourceutilization using an arbitrage operation.

FIG. 101 is a schematic diagram of an apparatus for improving resourceutilization using an arbitrage operation.

FIG. 102 is a schematic diagram of a system for improving resourcedistribution for a machine.

FIG. 103 is a schematic flow diagram of a system for improving resourcedistribution for a machine.

FIG. 104 is a schematic diagram of a system for improving aggregatedresource delivery for a fleet of machines.

FIG. 105 is a schematic flow diagram of a method for improvingaggregated resource delivery for a fleet of machines.

FIG. 106 is a schematic diagram of an apparatus for improving aggregatedresource delivery for a fleet of machines.

FIG. 107 is a schematic diagram of a system for improving resourcedelivery for a machine using a forward resource market.

FIG. 108 is a schematic flow diagram of a method for improving resourcedelivery for a machine using a forward resource market.

FIG. 109 is a schematic flow diagram of a method for improving resourcedelivery with a substitute resource.

DETAILED DESCRIPTION

Referring to FIG. 1, a set of systems, methods, components, modules,machines, articles, blocks, circuits, services, programs, applications,hardware, software and other elements are provided, collectivelyreferred to herein interchangeably as the system 100 or the platform100. The platform 100 enables a wide range of improvements of and forvarious machines, systems, and other components that enable transactionsinvolving the exchange of value (such as using currency, cryptocurrency,tokens, rewards or the like, as well as a wide range of in-kind andother resources) in various markets, including current or spot markets170, forward markets 130 and the like, for various goods, services, andresources. As used herein, “currency” should be understood to encompassfiat currency issued or regulated by governments, cryptocurrencies,tokens of value, tickets, loyalty points, rewards points, coupons,credits (e.g., regulatory, emissions, and/or industry recognizedexchangeable units of credit), abstracted versions of these (e.g., anarbitrary value index between parties understood to be usable in afuture transaction or the like), deliverables (e.g., service up-time,contractual delivery of goods or services including time values utilizedfor averaging or other measures of delivery which may then be exchangedbetween parties or utilized to offset other exchanged value), and otherelements that represent or may be exchanged for value. Resources, suchas ones that may be exchanged for value in a marketplace, should beunderstood to encompass goods, services, natural resources, energyresources, computing resources, energy storage resources, data storageresources, network bandwidth resources, processing resources and thelike, including resources for which value is exchanged and resourcesthat enable a transaction to occur (such as necessary computing andprocessing resources, storage resources, network resources, and energyresources that enable a transaction). Any currency and/or resources maybe abstracted to a uniform and/or normalized value scale, and mayadditionally or alternatively include a time aspect (e.g., calendardate, seasonal correction, and/or appropriate circumstances relevant tothe value of the currency and/or resources at the time of a transaction)and/or an exchange rate aspect (e.g., accounting for the value of acurrency or resource relative to another similar unit, such as currencyexchange rates between countries, discounting of a good or servicerelative to a most desired or requested configuration of the good orservice, etc.).

The platform 100 may include a set of forward purchase and sale machines110, each of which may be configured as an expert system or automatedintelligent agent for interaction with one or more of the set of spotmarkets 170 (e.g., reference FIG. 2A) and forward markets 130. Enablingthe set of forward purchase and sale machines 110 are an intelligentresource purchasing system 164 having a set of intelligent agents forpurchasing resources in spot and forward markets; an intelligentresource allocation and coordination engine 168 for the intelligent saleof allocated or coordinated resources, such as compute resources, energyresources, and other resources involved in or enabling a transaction; anintelligent sale engine 172 for intelligent coordination of a sale ofallocated resources in spot and futures markets; and an automated spotmarket testing and arbitrage transaction execution engine 194 forperforming spot testing of spot and forward markets, such as withmicro-transactions and, where conditions indicate favorable arbitrageconditions, automatically executing transactions in resources that takeadvantage of the favorable conditions. Each of the engines may usemodel-based or rule-based expert systems, such as based on rules orheuristics, as well as deep learning systems by which rules orheuristics may be learned over trials involving a large set of inputs.The engines may use any of the expert systems and artificialintelligence capabilities described throughout this disclosure.Interactions within the platform 100, including of all platformcomponents, and of interactions among them and with various markets, maybe tracked and collected, such as by a data aggregation system 144, suchas for aggregating data on purchases and sales in various marketplacesby the set of machines described herein. Aggregated data may includetracking and outcome data that may be fed to artificial intelligence andmachine learning systems, such as to train or supervise the same.

Operations to aggregate information as referenced throughout the presentdisclosure should be understood broadly. Example operations to aggregateinformation (e.g., data, purchasing, regulatory information, or anyother parameters) include, without limitation: summaries, averages ofdata values, selected binning of data, derivative information about data(e.g., rates of change, areas under a curve, changes in an indicatedstate based on the data, exceedance or conformance with a thresholdvalue, etc.), changes in the data (e.g., arrival of new information or anew type of information, information accrued in a defined or selectedtime period, etc.), and/or categorical descriptions about the data orother information related to the data). It will be understood that theexpression of aggregated information can be as desired, including atleast as graphical information, a report, stored raw data forutilization in generating displays and/or further use by an artificialintelligence and/or machine learning system, tables, and/or a datastream. In certain embodiments, aggregated data may be utilized by anexpert system, an artificial intelligence, and/or a machine learningsystem to perform various operations described throughout the presentdisclosure. Additionally or alternatively, expert systems, artificialintelligence, and/or machine learning systems may interact with theaggregated data, including determining which parameters are to beaggregated and/or the aggregation criteria to be utilized. For example,a machine learning system for a system utilizing a forward energypurchasing market may be configured to aggregate purchasing for thesystem. In the example, the machine learning system may be configured todetermine the signal effective parameters to incrementally improveand/or optimize purchasing decisions, and may additionally oralternatively change the aggregation parameters—for example binningcriteria for various components of a system (e.g., components thatrespond in a similar manner from the perspective of energyrequirements), determining the time frame of aggregation (e.g., weekly,monthly, seasonal, etc.), and/or changing a type of average, a referencerate for a rate of change of values in the system, or the like. Theprovided examples are provided for illustration, and are not limiting toany systems or operations described throughout the present disclosure.

The various engines may operate on a range of data sources, includingaggregated data from marketplace transactions, tracking data regardingthe behavior of each of the engines, and a set of external data sources182, which may include social media data sources 180 (such as socialnetworking sites like Facebook™ and Twitter™), Internet of Things (IoT)data sources (including from sensors, cameras, data collectors,appliances, personal devices, and/or instrumented machines and systems),such as IoT sources that provide information about machines and systemsthat enable transactions and machines and systems that are involved inproduction and consumption of resources. External data sources 182 mayinclude behavioral data sources, such as automated agent behavioral datasources 188 (such as tracking and reporting on behavior of automatedagents that are used for conversation and dialog management, agents usedfor control functions for machines and systems, agents used forpurchasing and sales, agents used for data collection, agents used foradvertising, and others), human behavioral data sources (such as datasources tracking online behavior, mobility behavior, energy consumptionbehavior, energy production behavior, network utilization behavior,compute and processing behavior, resource consumption behavior, resourceproduction behavior, purchasing behavior, attention behavior, socialbehavior, and others), and entity behavioral data sources 190 (such asbehavior of business organizations and other entities, such aspurchasing behavior, consumption behavior, production behavior, marketactivity, merger and acquisition behavior, transaction behavior,location behavior, and others). The IoT, social and behavioral data fromand about sensors, machines, humans, entities, and automated agents maycollectively be used to populate expert systems, machine learningsystems, and other intelligent systems and engines described throughoutthis disclosure, such as being provided as inputs to deep learningsystems and being provided as feedback or outcomes for purposes oftraining, supervision, and iterative improvement of systems forprediction, forecasting, classification, automation and control. Thedata may be organized as a stream of events. The data may be stored in adistributed ledger or other distributed system. The data may be storedin a knowledge graph where nodes represent entities and links representrelationships. The external data sources may be queried via variousdatabase query functions. The external data sources 182 may be accessedvia APIs, brokers, connectors, protocols like REST and SOAP, and otherdata ingestion and extraction techniques. Data may be enriched withmetadata and may be subject to transformation and loading into suitableforms for consumption by the engines, such as by cleansing,normalization, de-duplication and the like.

The platform 100 may include a set of intelligent forecasting engines192 for forecasting events, activities, variables, and parameters ofspot markets 170, forward markets 130, resources that are traded in suchmarkets, resources that enable such markets, behaviors (such as any ofthose tracked in the external data sources 182), transactions, and thelike. The intelligent forecasting engines 192 may operate on data fromthe data aggregation system 144 about elements of the platform 100 andon data from the external data sources 182. The platform may include aset of intelligent transaction engines 136 for automatically executingtransactions in spot markets 170 and forward markets 130. This mayinclude executing intelligent cryptocurrency transactions with anintelligent cryptocurrency execution engine 183 as described in moredetail below. The platform 100 may make use of asset of improveddistributed ledgers 113 and improved smart contracts 103, including onesthat embed and operate on proprietary information, instruction sets andthe like that enable complex transactions to occur among individualswith reduced (or without) reliance on intermediaries. In certainembodiments, the platform 100 may include a distributed processingarchitecture 146—for example distributing processing or compute tasksacross multiple processing devices, clusters, servers, and/orthird-party service devices or cloud devices. These and other componentsare described in more detail throughout this disclosure. In certainembodiments, one or more aspects of any of the platforms referenced inFIGS. 1 to 3 may be performed by any systems, apparatuses, controllers,or circuits as described throughout the present disclosure. In certainembodiments, one or more aspects of any of the platforms referenced inFIGS. 1 to 3 may include any procedures, methods, or operationsdescribed throughout the present disclosure. The example platformsdepicted in FIGS. 1 to 3 are illustrative, and any aspects may beomitted or altered while still providing one or more benefits asdescribed throughout the present disclosure.

Referring to the block diagrams of FIGS. 2A-2B, further details andadditional components of the platform 100 and interactions among themare depicted. The set of forward purchase and sale machines 110 mayinclude a regeneration capacity allocation engine 102 (such as forallocating energy generation or regeneration capacity, such as within ahybrid vehicle or system that includes energy generation or regenerationcapacity, a renewable energy system that has energy storage, or otherenergy storage system, where energy is allocated for one or more of saleon a forward market 130, sale in a spot market 170, use in completing atransaction (e.g., mining for cryptocurrency), or other purposes. Forexample, the regeneration capacity allocation engine 102 may exploreavailable options for use of stored energy, such as sale in current andforward energy markets (e.g., energy forward market 122, energy market148, energy storage forward market 174, and/or energy storage market178) that accept energy from producers, keeping the energy in storagefor future use, or using the energy for work (which may includeprocessing work, such as processing activities of the platform like datacollection or processing, or processing work for executing transactions,including mining activities for cryptocurrencies). In certainembodiments, the regeneration capacity allocation engine 102 includes atime value of stored energy, for example accounting for energy leakage(e.g., losses over time when stored), future useful work activities thatare expected to arise, competing factors that may affect the storedenergy (e.g., a release of reservoir water that is expected to occur ata future time for a non-energy purpose), and/or future energyregeneration that can be predicted that may affect the stored energyvalue proposition (e.g., energy storage will be exceeded if the energyis retained, and/or the value of available useful work activities willchange in a relevant time horizon). In certain embodiments, theregeneration capacity allocation engine 102 includes a rate value ofstored energy, for example accounting for the incremental cost orbenefit of utilizing stored energy rapidly (e.g., a low utilization ofenergy at the present time is cost effective, but a high utilization ofenergy at the present time is not cost effective). In certainembodiments, the regeneration capacity allocation engine 102 considersexternalities that are outside of the economic considerations of theimmediate system. For example, effects on a reservoir or downstreamriver bed due to energy utilization, grid capacity and/or grid dynamicseffects of providing or not providing energy from the energy storage,and/or system effects from providing or not providing energy (e.g.,ramping up server utilization with inexpensive energy immediately beforea long holiday that may result in overtime pay for service andmaintenance personnel), may affect the economic effectiveness ofaccepting, storing, or utilizing energy). The provided examples areprovided for illustration, and are not limiting to any systems oroperations described throughout the present disclosure.

The set of forward purchase and sale machines 110 may include an energypurchase and sale machine 104 for purchasing or selling energy, such asin an energy spot market 148 or an energy forward market 122. The energypurchase and sale machine 104 may use an expert system, neural networkor other intelligence to determine timing of purchases, such as based oncurrent and anticipated state information with respect to pricing andavailability of energy and based on current and anticipated stateinformation with respect to needs for energy, including needs for energyto perform computing tasks, cryptocurrency mining, data collectionactions, and other work, such as work done by automated agents andsystems and work required for humans or entities based on theirbehavior. For example, the energy purchase machine may recognize, bymachine learning, that a business is likely to require a block of energyin order to perform an increased level of manufacturing based on anincrease in orders or market demand and may purchase the energy at afavorable price on a futures market, based on a combination of energymarket data and entity behavioral data. Continuing the example, marketdemand may be understood by machine learning, such as by processinghuman behavioral data sources 184, such as social media posts,e-commerce data and the like that indicate increasing demand. The energypurchase and sale machine 104 may sell energy in the energy spot market148 or the energy forward market 122. Sale may also be conducted by anexpert system operating on the various data sources described herein,including with training on outcomes and human supervision.

The set of forward purchase and sale machines 110 may include arenewable energy credit (REC) purchase and sale machine 108, which maypurchase renewable energy credits, pollution credits, and otherenvironmental or regulatory credits in a spot market 150 or forwardmarket 124 for such credits. Purchasing may be configured and managed byan expert system operating on any of the external data sources 182 or ondata aggregated by the set of data aggregation systems 144 for theplatform. Renewable energy credits and other credits may be purchased byan automated system using an expert system, including machine learningor other artificial intelligence, such as where credits are purchasedwith favorable timing based on an understanding of supply and demandthat is determined by processing inputs from the data sources. Theexpert system may be trained on a data set of outcomes from purchasesunder historical input conditions. The expert system may be trained on adata set of human purchase decisions and/or may be supervised by one ormore human operators. The renewable energy credit (REC) purchase andsale machine 108 may also sell renewable energy credits, pollutioncredits, and other environmental or regulatory credits in a spot market150 or forward market 124 for such credits. Sale may also be conductedby an expert system operating on the various data sources describedherein, including with training on outcomes and human supervision.

The set of forward purchase and sale machines 110 may include anattention purchase and sale machine 112, which may purchase one or moreattention-related resources, such as advertising space, search listing,keyword listing, banner advertisements, participation in a panel orsurvey activity, participation in a trial or pilot, or the like in aspot market for attention 152 or a forward market for attention 128.Attention resources may include the attention of automated agents, suchas bots, crawlers, dialog managers, and the like that are used forsearching, shopping and purchasing. Purchasing of attention resourcesmay be configured and managed by an expert system operating on any ofthe external data sources 182 or on data aggregated by the set of dataaggregation systems 144 for the platform. Attention resources may bepurchased by an automated system using an expert system, includingmachine learning or other artificial intelligence, such as whereresources are purchased with favorable timing, such as based on anunderstanding of supply and demand, that is determined by processinginputs from the various data sources. For example, the attentionpurchase and sale machine 112 may purchase advertising space in aforward market for advertising based on learning from a wide range ofinputs about market conditions, behavior data, and data regardingactivities of agent and systems within the platform 100. The expertsystem may be trained on a data set of outcomes from purchases underhistorical input conditions. The expert system may be trained on a dataset of human purchase decisions and/or may be supervised by one or morehuman operators. The attention purchase and sale machine 112 may alsosell one or more attention-related resources, such as advertising space,search listing, keyword listing, banner advertisements, participation ina panel or survey activity, participation in a trial or pilot, or thelike in a spot market for attention 152 or a forward market forattention 128, which may include offering or selling access to, orattention or, one or more automated agents of the platform 100. Sale mayalso be conducted by an expert system operating on the various datasources described herein, including with training on outcomes and humansupervision.

The set of forward purchase and sale machines 110 may include a computepurchase and sale machine 114, which may purchase one or morecomputation-related resources, such as processing resources, databaseresources, computation resources, server resources, disk resources,input/output resources, temporary storage resources, memory resources,virtual machine resources, container resources, and others in a spotmarket for compute 154 or a forward market for compute 132. Purchasingof compute resources may be configured and managed by an expert systemoperating on any of the external data sources 182 or on data aggregatedby the set of data aggregation systems 144 for the platform. Computeresources may be purchased by an automated system using an expertsystem, including machine learning or other artificial intelligence,such as where resources are purchased with favorable timing, such asbased on an understanding of supply and demand, that is determined byprocessing inputs from the various data sources. For example, thecompute purchase and sale machine 114 may purchase or reserve computeresources on a cloud platform in a forward market for computer resourcesbased on learning from a wide range of inputs about market conditions,behavior data, and data regarding activities of agent and systems withinthe platform 100, such as to obtain such resources at favorable pricesduring surge periods of demand for computing. The expert system may betrained on a data set of outcomes from purchases under historical inputconditions. The expert system may be trained on a data set of humanpurchase decisions and/or may be supervised by one or more humanoperators. The compute purchase and sale machine 114 may also sell oneor more computation-related resources that are connected to, part of, ormanaged by the platform 100, such as processing resources, databaseresources, computation resources, server resources, disk resources,input/output resources, temporary storage resources, memory resources,virtual machine resources, container resources, and others in a spotmarket for compute 154 or a forward market for compute 132. Sale mayalso be conducted by an expert system operating on the various datasources described herein, including with training on outcomes and humansupervision.

The set of forward purchase and sale machines 110 may include a datastorage purchase and sale machine 118, which may purchase one or moredata-related resources, such as database resources, disk resources,server resources, memory resources, RAM resources, network attachedstorage resources, storage attached network (SAN) resources, taperesources, time-based data access resources, virtual machine resources,container resources, and others in a spot market for data storage 158 ora forward market for data storage 134. Purchasing of data storageresources may be configured and managed by an expert system operating onany of the external data sources 182 or on data aggregated by the set ofdata aggregation systems 144 for the platform. Data storage resourcesmay be purchased by an automated system using an expert system,including machine learning or other artificial intelligence, such aswhere resources are purchased with favorable timing, such as based on anunderstanding of supply and demand, that is determined by processinginputs from the various data sources. For example, the compute purchaseand sale machine 114 may purchase or reserve compute resources on acloud platform in a forward market for compute resources based onlearning from a wide range of inputs about market conditions, behaviordata, and data regarding activities of agent and systems within theplatform 100, such as to obtain such resources at favorable pricesduring surge periods of demand for storage. The expert system may betrained on a data set of outcomes from purchases under historical inputconditions. The expert system may be trained on a data set of humanpurchase decisions and/or may be supervised by one or more humanoperators. The data storage purchase and sale machine 118 may also sellone or more data storage-related resources that are connected to, partof, or managed by the platform 100 in a spot market for data storage 158or a forward market for data storage 134. Sale may also be conducted byan expert system operating on the various data sources described herein,including with training on outcomes and human supervision.

The set of forward purchase and sale machines 110 may include abandwidth purchase and sale machine 120, which may purchase one or morebandwidth-related resources, such as cellular bandwidth, Wifi bandwidth,radio bandwidth, access point bandwidth, beacon bandwidth, local areanetwork bandwidth, wide area network bandwidth, enterprise networkbandwidth, server bandwidth, storage input/output bandwidth, advertisingnetwork bandwidth, market bandwidth, or other bandwidth, in a spotmarket for bandwidth 160 or a forward market for bandwidth 138.Purchasing of bandwidth resources may be configured and managed by anexpert system operating on any of the external data sources 182 or ondata aggregated by the set of data aggregation systems 144 for theplatform. Bandwidth resources may be purchased by an automated systemusing an expert system, including machine learning or other artificialintelligence, such as where resources are purchased with favorabletiming, such as based on an understanding of supply and demand, that isdetermined by processing inputs from the various data sources. Forexample, the bandwidth purchase and sale machine 120 may purchase orreserve bandwidth on a network resource for a future networking activitymanaged by the platform based on learning from a wide range of inputsabout market conditions, behavior data, and data regarding activities ofagent and systems within the platform 100, such as to obtain suchresources at favorable prices during surge periods of demand forbandwidth. The expert system may be trained on a data set of outcomesfrom purchases under historical input conditions. The expert system maybe trained on a data set of human purchase decisions and/or may besupervised by one or more human operators. The bandwidth purchase andsale machine 120 may also sell one or more bandwidth-related resourcesthat are connected to, part of, or managed by the platform 100 in a spotmarket for bandwidth 160 or a forward market for bandwidth 138. Sale mayalso be conducted by an expert system operating on the various datasources described herein, including with training on outcomes and humansupervision.

The set of forward purchase and sale machines 110 may include a spectrumpurchase and sale machine 142, which may purchase one or morespectrum-related resources, such as cellular spectrum, 3G spectrum, 4Gspectrum, LTE spectrum, 5G spectrum, cognitive radio spectrum,peer-to-peer network spectrum, emergency responder spectrum and the likein a spot market for spectrum 162 or a forward market for spectrum 140.In certain embodiments, a spectrum related resource may relate to anon-wireless communication protocol, such as frequency stacking on ahard line (e.g., a copper wire or optical fiber). Purchasing of spectrumresources may be configured and managed by an expert system operating onany of the external data sources 182 or on data aggregated by the set ofdata aggregation systems 144 for the platform. Spectrum resources may bepurchased by an automated system using an expert system, includingmachine learning or other artificial intelligence, such as whereresources are purchased with favorable timing, such as based on anunderstanding of supply and demand, that is determined by processinginputs from the various data sources. For example, the spectrum purchaseand sale machine 142 may purchase or reserve spectrum on a networkresource for a future networking activity managed by the platform basedon learning from a wide range of inputs about market conditions,behavior data, and data regarding activities of agent and systems withinthe platform 100, such as to obtain such resources at favorable pricesduring surge periods of demand for spectrum. The expert system may betrained on a data set of outcomes from purchases under historical inputconditions. The expert system may be trained on a data set of humanpurchase decisions and/or may be supervised by one or more humanoperators. The spectrum purchase and sale machine 142 may also sell oneor more spectrum-related resources that are connected to, part of, ormanaged by the platform 100 in a spot market for spectrum 162 or aforward market for spectrum 140. Sale may also be conducted by an expertsystem operating on the various data sources described herein, includingwith training on outcomes and human supervision.

In embodiments, the intelligent resource allocation and coordinationengine 168, including the intelligent resource purchasing system 164,the intelligent sale engine 172 and the automated spot market testingand arbitrage transaction execution engine 194, may provide coordinatedand automated allocation of resources and coordinated execution oftransactions across the various forward markets 130 and spot markets 170by coordinating the various purchase and sale machines, such as by anexpert system, such as a machine learning system (which may model-basedor a deep learning system, and which may be trained on outcomes and/orsupervised by humans). For example, the allocation and coordinationengine 168 may coordinate purchasing of resources for a set of assetsand coordinated sale of resources available from a set of assets, suchas a fleet of vehicles, a data center of processing and data storageresources, an information technology network (on premises, cloud, orhybrids), a fleet of energy production systems (renewable ornon-renewable), a smart home or building (including appliances,machines, infrastructure components and systems, and the like thereofthat consume or produce resources), and the like.

The platform 100 may incrementally improve or optimize allocation ofresource purchasing, sale and utilization based on data aggregated inthe platform, such as by tracking activities of various engines andagents, as well as by taking inputs from external data sources 182. Inembodiments, outcomes may be provided as feedback for training theintelligent resource allocation and coordination engine 168, such asoutcomes based on yield, profitability, optimization of resources,optimization of business objectives, satisfaction of goals, satisfactionof users or operators, or the like. For example, as the energy forcomputational tasks becomes a significant fraction of an enterprise'senergy usage, the platform 100 may learn to optimize how a set ofmachines that have energy storage capacity allocate that capacity amongcomputing tasks (such as for cryptocurrency mining, application ofneural networks, computation on data and the like), other useful tasks(that may yield profits or other benefits), storage for future use, orsale to the provider of an energy grid. The platform 100 may be used byfleet operators, enterprises, governments, municipalities, militaryunits, first responder units, manufacturers, energy producers, cloudplatform providers, and other enterprises and operators that own oroperate resources that consume or provide energy, computation, datastorage, bandwidth, or spectrum. The platform 100 may also be used inconnection with markets for attention, such as to use available capacityof resources to support attention-based exchanges of value, such as inadvertising markets, micro-transaction markets, and others.

Operations to optimize, as used throughout the present disclosure,should be understood broadly. In certain embodiments, operations tooptimize include operations to improve outcomes, including incrementaland/or iterative improvements. In certain embodiments, optimization caninclude operations to improve outcomes until a threshold improvementlevel is reached (e.g., a success criteria is met, further improvementsare below a threshold level of improvement, a particular outcome isimproved by a threshold amount, etc.). In certain embodiments,optimization may be performed utilizing a cost and benefit analysis,where cost is in actual currency, a normalized cost value, a cost indexconfigured to describe the resources, time, and/or lost opportunity of aparticular action, or any other cost description. In certainembodiments, benefits may be in actual currency, a normalized benefitvalue, a benefit index, or any other measure or description of thebenefit of a particular action. In certain embodiments, other parameterssuch as the time value and/or time trajectory of costs or benefits maybe included in the optimization—for example as a limiting value (e.g.,optimization is the best value after 5 minutes of computations) and/oras a factor (e.g., a growing cost or shrinking benefit is applied asoptimization analyses progress) in the optimization process. Anyoperations utilizing artificial intelligence, expert systems, machinelearning, and/or any other systems or operations described throughoutthe present disclosure that incrementally improve, iteratively improve,and/or formally optimize parameters are understood as examples ofoptimization and/or improvement herein. One of skill in the art, havingthe benefit of the present disclosure and information ordinarilyavailable when contemplating a particular system, can readily determineparameters and criteria for optimization for a particular system.Certain considerations that may be relevant to a particular systeminclude, without limitation: the cost of resource utilization includingtime values and/or time trajectories of those costs; the benefits of theaction goals (e.g., selling resources, completing calculations,providing bandwidth, etc.) including time values and/or timetrajectories of those benefits; seasonal, periodic, and/or episodiceffects on the availability of resources and/or the demand forresources; costs of capitalization for a system and/or for a servicingsystem (e.g., costs to add computing resources, and/or costs for aservice provider to add computing resources); operating costs forutilization of resources, including time values, time trajectories, andexternalities such as personnel, maintenance, and incrementalutilization of service life for resources; capacity of resourceproviders and/or cost curves for resource utilization; diminishingreturn curves and/or other external effects for benefit provisions(e.g., the 100^(th) unit of computation may pay less than the 50^(th)unit of computation for a particular system, and/or the ability toprovide 100 units of computation may open other markets and/or allow forservicing of a different customer base than the ability to provide only50 units of computation); and/or risk factors related to resourceutilization (e.g., increasing data storage at a single location mayincrease risk over distributed data; increasing throughput of a systemmay change the risks, such as increased traffic, higher operating pointsfor systems, increased risk of regulatory violations, or the like).

Referring still to FIGS. 2A-2B, the platform 100 may include a set ofintelligent forecasting engines 192 that forecast one or moreattributes, parameters, variables, or other factors, such as for use asinputs by the set of forward purchase and sale machines, the intelligenttransaction engines 136 (such as for intelligent cryptocurrencyexecution) or for other purposes. Each of the set of intelligentforecasting engines 192 may use data that is tracked, aggregated,processed, or handled within the platform 100, such as by the dataaggregation system 144, as well as input data from external data sources182, such as social media data sources 180, automated agent behavioraldata sources 188, human behavioral data sources 184, entity behavioraldata sources 190 and IoT data sources 198. These collective inputs maybe used to forecast attributes, such as using a model (e.g., Bayesian,regression, or other statistical model), a rule, or an expert system,such as a machine learning system that has one or more classifiers,pattern recognizers, and predictors, such as any of the expert systemsdescribed throughout this disclosure. In embodiments, the set ofintelligent forecasting engines 192 may include one or more specializedengines that forecast market attributes, such as capacity, demand,supply, and prices, using particular data sources for particularmarkets. These may include an energy price forecasting engine 215 thatbases its forecast on behavior of an automated agent, a network spectrumprice forecasting engine 217 that bases its forecast on behavior of anautomated agent, a REC price forecasting engine 219 that bases itsforecast on behavior of an automated agent, a compute price forecastingengine 221 that bases its forecast on behavior of an automated agent, anetwork spectrum price forecasting engine 223 that bases its forecast onbehavior of an automated agent. In each case, observations regarding thebehavior of automated agents, such as ones used for conversation, fordialog management, for managing electronic commerce, for managingadvertising and others may be provided as inputs for forecasting to theengines. The intelligent forecasting engines 192 may also include arange of engines that provide forecasts at least in part based on entitybehavior, such as behavior of business and other organizations, such asmarketing behavior, sales behavior, product offering behavior,advertising behavior, purchasing behavior, transactional behavior,merger and acquisition behavior, and other entity behavior. These mayinclude an energy price forecasting engine 225 using entity behavior, anetwork spectrum price forecasting engine 227 using entity behavior, aREC price forecasting engine 229 using entity behavior, a compute priceforecasting engine 231 using entity behavior, and a network spectrumprice forecasting engine 233 using entity behavior.

The intelligent forecasting engines 192 may also include a range ofengines that provide forecasts at least in part based on human behavior,such as behavior of consumers and users, such as purchasing behavior,shopping behavior, sales behavior, product interaction behavior, energyutilization behavior, mobility behavior, activity level behavior,activity type behavior, transactional behavior, and other humanbehavior. These may include an energy price forecasting engine 235 usinghuman behavior, a network spectrum price forecasting engine 237 usinghuman behavior, a REC price forecasting engine 239 using human behavior,a compute price forecasting engine 241 using human behavior, and anetwork spectrum price forecasting engine 243 using human behavior.

Referring still to FIGS. 2A-2B, the platform 100 may include a set ofintelligent transaction engines 136 that automate execution oftransactions in forward markets 130 and/or spot markets 170 based ondetermination that favorable conditions exist, such as by theintelligent resource allocation and coordination engine 168 and/or withuse of forecasts form the intelligent forecasting engines 192. Theintelligent transaction engines 136 may be configured to automaticallyexecute transactions, using available market interfaces, such as APIs,connectors, ports, network interfaces, and the like, in each of themarkets noted above. In embodiments, the intelligent transaction enginesmay execute transactions based on event streams that come from externaldata sources 182, such as IoT data sources 198 and social media datasources 180. The engines may include, for example, an IoT forward energytransaction engine 195 and/or an IoT compute market transaction engine106, either or both of which may use data 185 from the Internet ofThings (IoT) to determine timing and other attributes for markettransaction in a market for one or more of the resources describedherein, such as an energy market transaction, a compute resourcetransaction or other resource transaction. IoT data 185 may includeinstrumentation and controls data for one or more machines (optionallycoordinated as a fleet) that use or produce energy or that use or havecompute resources, weather data that influences energy prices orconsumption (such as wind data influencing production of wind energy),sensor data from energy production environments, sensor data from pointsof use for energy or compute resources (such as vehicle traffic data,network traffic data, IT network utilization data, Internet utilizationand traffic data, camera data from work sites, smart building data,smart home data, and the like), and other data collected by ortransferred within the Internet of Things, including data stored in IoTplatforms and of cloud services providers like Amazon, IBM, and others.The intelligent transaction engines 136 may include engines that usesocial data to determine timing of other attributes for a markettransaction in one or more of the resources described herein, such as asocial data forward energy transaction engine 199 and/or a social datafor compute market transaction engine 116. Social data may include datafrom social networking sites (e.g., Facebook™, YouTube™, Twitter™,Snapchat™, Instagram™, and others), data from websites, data frome-commerce sites, and data from other sites that contain informationthat may be relevant to determining or forecasting behavior of users orentities, such as data indicating interest or attention to particulartopics, goods or services, data indicating activity types and levels,such as may be observed by machine processing of image data showingindividuals engaged in activities, including travel, work activities,leisure activities, and the like. Social data may be supplied to machinelearning, such as for learning user behavior or entity behavior, and/oras an input to an expert system, a model, or the like, such as one fordetermining, based on the social data, the parameters for a transaction.For example, an event or set of events in a social data stream mayindicate the likelihood of a surge of interest in an online resource, aproduct, or a service, and compute resources, bandwidth, storage, orlike may be purchased in advance (avoiding surge pricing) to accommodatethe increased interest reflected by the social data market predictor186.

Referring to FIG. 3, the platform 100 may include capabilities fortransaction execution that involve one or more distributed ledgers 113and one or more smart contracts 103, where the distributed ledgers 113and smart contracts 103 are configured to enable specialized transactionfeatures for specific transaction domains. One such domain isintellectual property, which transactions are highly complex, involvinglicensing terms and conditions that are somewhat difficult to manage, ascompared to more straightforward sales of goods or services. Inembodiments, a smart contract wrapper, such as wrapper aggregatingintellectual property 105, is provided, using a distributed ledger,wherein the smart contract embeds IP licensing terms for intellectualproperty that is embedded in the distributed ledger and whereinexecuting an operation on the distributed ledger provides access to theintellectual property and commits the executing party to the IPlicensing terms. Licensing terms for a wide range of goods and services,including digital goods like video, audio, video game, video gameelement, music, electronic book and other digital goods may be managedby tracking transactions involving them on a distributed ledger, wherebypublishers may verify a chain of licensing and sublicensing. Thedistributed ledger may be configured to add each licensee to the ledger,and the ledger may be retrieved at the point of use of a digital item,such as in a streaming platform, to validate that licensing hasoccurred.

In embodiments, an improved distributed ledger is provided with thesmart contract wrapper, such as an IP wrapper 105, container, smartcontract or similar mechanism for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toan aggregate stack of intellectual property. In many cases, intellectualproperty builds on other intellectual property, such as where softwarecode is derived from other code, where trade secrets or know-how 109 forelements of a process are combined to enable a larger process, wherepatents covering sub-components of a system or steps in a process arepooled, where elements of a video game include sub-component assets fromdifferent creators, where a book contains contributions from multipleauthors, and the like. In embodiments, a smart IP wrapper aggregateslicensing terms for different intellectual property items (includingdigital goods, including ones embodying different types of intellectualproperty rights, and transaction data involving the item), as well asoptionally one or more portions of the item corresponding to thetransaction data, are stored in a distributed ledger that is configuredto enable validation of agreement to the licensing terms (such as at apoint of use) and/or access control to the item. In certain embodiments,a smart IP wrapper may include sub-licenses, dependent licenses,verification of ownership and chain of title, and/or any other featuresthat ensure that a license is valid and is able to be used. Inembodiments, a royalty apportionment wrapper 115 may be provided in asystem having a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual propertyand to agree to an apportionment of royalties among the parties in theledger. Thus, a ledger may accumulate contributions to the ledger alongwith evidence of agreement to the apportionment of any royalties amongthe contributors of the IP that is embedded in and/or controlled by theledger. The ledger may record licensing terms and automatically varythem as new contributions are made, such as by one or more rules. Forexample, contributors may be given a share of a royalty stack accordingto a rule, such as based on a fractional contribution, such as based onlines of code contributed, a number and/or value of effective operationscontributed from a set of operations performed by one or more computerprograms, a valuation contribution from a particular IP element into alarger good or service provided under the license or license group,lines of authorship, contribution to components of a system, and thelike. In embodiments, a distributed ledger may be forked into versionsthat represent varying combinations of sub-components of IP, such as toallow users to select combinations that are of most use, therebyallowing contributors who have contributed the most value to berewarded. Variation and outcome tracking may be iteratively improved,such as by machine learning. In certain embodiments, operations on adistributed ledger may include updating the licensing terms, valuations,and/or royalty shares according to external data, such as litigationand/or administrative decisions (e.g., from a patent or trademarkoffice) that may affect intellectual property assets (e.g., increasing avalidity estimate, determining an asset is invalid or unenforceable,and/or creating a determined valuation for the asset), changes ofownership, expiration and/or aging of assets, and/or changing of assetstatus (e.g., a patent application issuing as a patent).

In embodiments, a distributed ledger is provided for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual propertyand/or to determine the relationship of the contributed intellectualproperty to the aggregate stack and to royalty generating elementsrelated to the aggregate stack such as goods or services sold using thelicensing terms. In certain embodiments, operations on the ledger updatethe relationships of various elements of intellectual property in theaggregate stack in response to additions to the stack—for example wherea newly contributed element of intellectual property replaces an olderone for certain goods or services, and/or changes the value propositionfor intellectual property elements already in the aggregate stack.

In embodiments, the platform 100 may have an improved distributed ledgerfor aggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to commit a party to a contract term via an IP transactionwrapper 119 of the ledger. This may include operations involvingcryptocurrencies, tokens, or other operations, as well as conventionalpayments and in-kind transfers, such as of various resources describedherein. The ledger may accumulate evidence of commitments to IPtransactions by parties, such as entering into royalty terms, revenuesharing terms, IP ownership terms, warranty and liability terms, licensepermissions and restrictions, field of use terms, and many others. Incertain embodiments, the ledger may accumulate transactional databetween parties which may include costs and/or payments in any form,including abstract or indexed valuations that may be converted tocurrency and/or traded goods or services at a later time. In certainembodiments, the ledger may additionally or alternatively includegeographic information (e.g., where a transaction occurred, wherecontractual acceptance is deemed to have occurred, where goods orservices were performed/delivered, and/or where related data is stored),entity information (e.g., which entities, sub-entities, and/oraffiliates are involved in licenses and transactions), and/or timeinformation (e.g., when acceptance occurs, when licensing and updatesoccur, when goods and services are ordered, when contractual terms orpayments are committed, and/or when contractual terms or payments aredelivered). It can be seen that the use of improved distributed ledgersthroughout the disclosure supports numerous improvements over previouslyknown systems, including at least improved management of licensingagreements, tax management, contract management, data security,regulatory compliance, confidence that the agreed terms are correct onthe merits, and confidence that the agreed terms are implementedproperly.

In embodiments, improved distributed ledgers may include ones having atokenized instruction set, such that operation on the distributed ledgerprovides provable access to the instruction set. A party wishing toshare permission to know how, a trade secret or other valuableinstructions may thus share the instruction set via a distributed ledgerthat captures and stores evidence of an action on the ledger by a thirdparty, thereby evidencing access and agreement to terms and conditionsof access. In embodiments, the platform 100 may have a distributedledger that tokenizes executable algorithmic logic 121, such thatoperation on the distributed ledger provides provable access to theexecutable algorithmic logic. A variety of instruction sets may bestored by a distributed ledger, such as to verify access and verifyagreement to terms (such as smart contract terms). In embodiments,instruction sets that embody trade secrets may be separated intosub-components, so that operations must occur on multiple ledgers to get(provable) access to a trade secret. This may permit parties wishing toshare secrets, such as with multiple sub-contractors or vendors, tomaintain provable access control, while separating components amongdifferent vendors to avoid sharing an entire set with a single party.Various kinds of executable instruction sets may be stored onspecialized distributed ledgers that may include smart wrappers forspecific types of instruction sets, such that provable access control,validation of terms, and tracking of utilization may be performed byoperations on the distributed ledger (which may include triggeringaccess controls within a content management system or other systems uponvalidation of actions taken in a smart contract on the ledger. Inembodiments, the platform 100 may have a distributed ledger thattokenizes a 3D printer instruction set 123, such that operation on thedistributed ledger provides provable access to the instruction set.

In embodiments, the platform 100 may have a distributed ledger thattokenizes an instruction set for a coating process 125, such thatoperation on the distributed ledger provides provable access to theinstruction set.

In embodiments, the platform 100 may have a distributed ledger thattokenizes an instruction set for a semiconductor fabrication process129, such that operation on the distributed ledger provides provableaccess to the fabrication process.

In embodiments, the platform 100 may have a distributed ledger thattokenizes a firmware program 131, such that operation on the distributedledger provides provable access to the firmware program.

In embodiments, the platform 100 may have a distributed ledger thattokenizes an instruction set for an FPGA 133, such that operation on thedistributed ledger provides provable access to the FPGA.

In embodiments, the platform 100 may have a distributed ledger thattokenizes serverless code logic 135, such that operation on thedistributed ledger provides provable access to the serverless codelogic.

In embodiments, the platform 100 may have a distributed ledger thattokenizes an instruction set for a crystal fabrication system 139, suchthat operation on the distributed ledger provides provable access to theinstruction set.

In embodiments, the platform 100 may have a distributed ledger thattokenizes an instruction set for a food preparation process 141, suchthat operation on the distributed ledger provides provable access to theinstruction set.

In embodiments, the platform 100 may have a distributed ledger thattokenizes an instruction set for a polymer production process 143, suchthat operation on the distributed ledger provides provable access to theinstruction set.

In embodiments, the platform 100 may have a distributed ledger thattokenizes an instruction set for chemical synthesis process 145, suchthat operation on the distributed ledger provides provable access to theinstruction set.

In embodiments, the platform 100 may have a distributed ledger thattokenizes an instruction set for a biological production process 149,such that operation on the distributed ledger provides provable accessto the instruction set.

In embodiments, the platform 100 may have a distributed ledger thattokenizes a trade secret with an expert wrapper 151, such that operationon the distributed ledger provides provable access to the trade secretand the wrapper provides validation of the trade secret by the expert.An interface may be provided by which an expert accesses the tradesecret on the ledger and verifies that the information is accurate andsufficient to allow a third party to use the secret.

In embodiments, the platform 100 may have a distributed ledger thatincludes instruction ledger operation analytics 159, for exampleproviding aggregate views 155 of a trade secret into a chain that proveswhich and how many parties have viewed the trade secret. Views may beused to allocate value to creators of the trade secret, to operators ofthe platform 100, or the like. In embodiments, the platform 100 may havea distributed ledger that determines an instruction access probability157, such as a chance that an instruction set or other IP element hasbeen accessed, will be accessed, and/or will be accessed in a given timeframe (e.g., the next day, next week, next month, etc.).

In embodiments, the platform 100 may have a distributed ledger thattokenizes an instruction set 111, such that operation on the distributedledger provides provable access (e.g., presented as views 155) to theinstruction set 111 and execution of the instruction set 161 on a systemresults in recording a transaction in the distributed ledger.

In embodiments, the platform 100 may have a distributed ledger thattokenizes an item of intellectual property and a reporting system thatreports an analytic result based on the operations performed on thedistributed ledger or the intellectual property, for example using theinstruction ledger operations analytics. In certain embodiments,analytics may additionally or alternatively be provided for anydistributed ledger and data stored thereon, such as IP, algorithmiclogic, or any other distributed ledger operations described throughoutthe present disclosure.

In embodiments, the platform 100 may have a distributed ledger thataggregates a set of instructions, where an operation on the distributedledger adds at least one instruction to a pre-existing set ofinstructions 161 to provide a modified set of instructions.

Referring still to FIG. 3, an intelligent cryptocurrency executionengine 183 may provide intelligence for the timing, location and otherattributes of a cryptocurrency transaction, such as a miningtransaction, an exchange transaction, a storage transaction, a retrievaltransaction, or the like. Cryptocurrencies like Bitcoin™ areincreasingly widespread, with specialized coins having emerged for awide variety of purposes, such as exchanging value in variousspecialized market domains. Initial offerings of such coins, or ICOs,are increasingly subject to regulations, such as securities regulations,and in some cases to taxation. Thus, while cryptocurrency transactionstypically occur within computer networks, jurisdictional factors may beimportant in determining where, when and how to execute a transaction,store a cryptocurrency, exchange it for value. In embodiments,intelligent cryptocurrency execution engine 183 may use featuresembedded in or wrapped around the digital object representing a coin,such as features that cause the execution of transactions in the coin tobe undertaken with awareness of various conditions, including geographicconditions, regulatory conditions, tax conditions, market conditions,IoT data for cryptotransaction 295 and social data for cryptotransaction193, and the like.

In embodiments, the platform 100 may include a tax aware coin 165 orsmart wrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location.

In embodiments, the platform 100 may include a location-aware coin 169or smart wrapper that enables a self-executing cryptocurrency coin thatcommits a transaction upon recognizing a location-based parameter thatprovides favorable tax treatment.

In embodiments, the platform 100 may include an expert system or AIagent for tax-aware coin usage 171 that uses machine learning tooptimize the execution of cryptocurrency transactions based on taxstatus. Machine learning may use one or more models or heuristics, suchas populated with relevant jurisdictional tax data, may be trained on atraining set of human trading operations, may be supervised by humansupervisors, and/or may use a deep learning technique based on outcomesover time, such as when operating on a wide range of internal systemdata and external data sources 182 as described throughout thisdisclosure.

In embodiments, the platform 100 may include regulation aware coin 173having a coin, a smart wrapper, and/or an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information. Machine learning may use one or more models orheuristics, such as populated with relevant jurisdictional regulatorydata, may be trained on a training set of human trading operations, maybe supervised by human supervisors, and/or may use a deep learningtechnique based on outcomes over time, such as when operating on a widerange of internal system data and external data sources 182 as describedthroughout this disclosure.

In embodiments, the platform 100 may include an energy price-aware coin175, wrapper, or expert system that uses machine learning to optimizethe execution of a cryptocurrency transaction based on real time energyprice information for an available energy source. Cryptocurrencytransactions, such as coin mining and blockchain operations, may behighly energy intensive. An energy price-aware coin may be configured totime such operations based on energy price forecasts, such as with oneor more of the intelligent forecasting engines 192 described throughoutthis disclosure.

In embodiments, the platform 100 may include an energy source aware coin179, wrapper, or expert system that uses machine learning to optimizethe execution of a cryptocurrency transaction based on an understandingof available energy sources to power computing resources to execute thetransaction. For example, coin mining may be performed only whenrenewable energy sources are available. Machine learning foroptimization of a transaction may use one or more models or heuristics,such as populated with relevant energy source data (such as may becaptured in a knowledge graph, which may contain energy sourceinformation by type, location and operating parameters), may be trainedon a training set of input-output data for human-initiated transactions,may be supervised by human supervisors, and/or may use a deep learningtechnique based on outcomes over time, such as when operating on a widerange of internal system data and external data sources 182 as describedthroughout this disclosure.

In embodiments, the platform 100 may include a charging cycle aware coin181, wrapper, or an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction. Forexample, a battery may be discharged for a cryptocurrency transactiononly if a minimum threshold of battery charge is maintained for otheroperational use, if re-charging resources are known to be readilyavailable, or the like. Machine learning for optimization of chargingand recharging may use one or more models or heuristics, such aspopulated with relevant battery data (such as may be captured in aknowledge graph, which may contain energy source information by type,location and operating parameters), may be trained on a training set ofhuman operations, may be supervised by human supervisors, and/or may usea deep learning technique based on outcomes over time, such as whenoperating on a wide range of internal system data and external datasources 182 as described throughout this disclosure.

Optimization of various intelligent coin operations may occur withmachine learning that is trained on outcomes, such as financialprofitability. Any of the machine learning systems described throughoutthis disclosure may be used for optimization of intelligentcryptocurrency transaction management.

In embodiments, compute resources, such as those mentioned throughoutthis disclosure, may be allocated to perform a range of computing tasks,both for operations that occur within the platform 100, ones that aremanaged by the platform, and ones that involve the activities, workflowsand processes of various assets that may be owned, operated or managedin conjunction with the platform, such as sets or fleets of assets thathave or use computing resources. Examples of compute tasks include,without limitation, cryptocurrency mining, distributed ledgercalculations and storage, forecasting tasks, transaction executiontasks, spot market testing tasks, internal data collection tasks,external data collection, machine learning tasks, and others. As notedabove, energy, compute resources, bandwidth, spectrum, and otherresources may be coordinated, such as by machine learning, for thesetasks. Outcome and feedback information may be provided for the machinelearning, such as outcomes for any of the individual tasks and overalloutcomes, such as yield and profitability for business or otheroperations involving the tasks.

In embodiments, networking resources, such as those mentioned throughoutthis disclosure, may be allocated to perform a range of networkingtasks, both for operations that occur within the platform 100, ones thatare managed by the platform, and ones that involve the activities,workflows and processes of various assets that may be owned, operated ormanaged in conjunction with the platform, such as sets or fleets ofassets that have or use networking resources. Examples of networkingtasks include cognitive network coordination, network coding, peerbandwidth sharing (including, for example cost-based routing,value-based routing, outcome-based routing and the like), distributedtransaction execution, spot market testing, randomization (e.g., usinggenetic programming with outcome feedback to vary network configurationsand transmission paths), internal data collection and external datacollection. As noted above, energy, compute resources, bandwidth,spectrum, and other resources may be coordinated, such as by machinelearning, for these networking tasks. Outcome and feedback informationmay be provided for the machine learning, such as outcomes for any ofthe individual tasks and overall outcomes, such as yield andprofitability for business or other operations involving the tasks.

In embodiments, data storage resources, such as those mentionedthroughout this disclosure, may be allocated to perform a range of datastorage tasks, both for operations that occur within the platform 100,ones that are managed by the platform, and ones that involve theactivities, workflows and processes of various assets that may be owned,operated or managed in conjunction with the platform, such as sets orfleets of assets that have or use networking resources. Examples of datastorage tasks include distributed ledger storage, storage of internaldata (such as operational data with the platform), cryptocurrencystorage, smart wrapper storage, storage of external data, storage offeedback and outcome data, and others. As noted above, data storage,energy, compute resources, bandwidth, spectrum, and other resources maybe coordinated, such as by machine learning, for these data storagetasks. Outcome and feedback information may be provided for the machinelearning, such as outcomes for any of the individual tasks and overalloutcomes, such as yield and profitability for business or otheroperations involving the tasks.

In embodiments, smart contracts, such as ones embodying terms relatingto intellectual property, trade secrets, know how, instruction sets,algorithmic logic, and the like may embody or include contract terms,which may include terms and conditions for options, royalty stackingterms, field exclusivity, partial exclusivity, pooling of intellectualproperty, standards terms (such as relating to essential andnon-essential patent usage), technology transfer terms, consultingservice terms, update terms, support terms, maintenance terms,derivative works terms, copying terms, and performance-related rights ormetrics, among many others.

In embodiments where an instruction set is embodied in digital form,such as contained in or managed by a distributed ledger transactionssystem, various systems may be configured with interfaces that allowthem to access and use the instruction sets. In embodiments, suchsystems may include access control features that validate properlicensing by inspection of a distributed ledger, a key, a token, or thelike that indicates the presence of access rights to an instruction set.Such systems that execute distributed instruction sets may includesystems for 3D printing, crystal fabrication, semiconductor fabrication,coating items, producing polymers, chemical synthesis and biologicalproduction, among others.

Networking capabilities and network resources should be understood toinclude a wide range of networking systems, components and capabilities,including infrastructure elements for 3G, 4G, LTE, 5G and other cellularnetwork types, access points, routers, and other WiFi elements,cognitive networking systems and components, mobile networking systemsand components, physical layer, MAC layer and application layer systemsand components, cognitive networking components and capabilities,peer-to-peer networking components and capabilities, optical networkingcomponents and capabilities, and others.

Referring to FIG. 4 through FIG. 31, embodiments of the presentdisclosure, including ones involving expert systems, self-organization,machine learning, artificial intelligence, and the like, may benefitfrom the use of a neural net, such as a neural net trained for patternrecognition, for classification of one or more parameters,characteristics, or phenomena, for support of autonomous control, andother purposes. References to a neural net throughout this disclosureshould be understood to encompass a wide range of different types ofneural networks, machine learning systems, artificial intelligencesystems, and the like, such as feed forward neural networks, radialbasis function neural networks, self-organizing neural networks (e.g.,Kohonen self-organizing neural networks), recurrent neural networks,modular neural networks, artificial neural networks, physical neuralnetworks, multi-layered neural networks, convolutional neural networks,hybrids of neural networks with other expert systems (e.g., hybrid fuzzylogic—neural network systems), Autoencoder neural networks,probabilistic neural networks, time delay neural networks, convolutionalneural networks, regulatory feedback neural networks, radial basisfunction neural networks, recurrent neural networks, Hopfield neuralnetworks, Boltzmann machine neural networks, self-organizing map (SOM)neural networks, learning vector quantization (LVQ) neural networks,fully recurrent neural networks, simple recurrent neural networks, echostate neural networks, long short-term memory neural networks,bi-directional neural networks, hierarchical neural networks, stochasticneural networks, genetic scale RNN neural networks, committee ofmachines neural networks, associative neural networks, physical neuralnetworks, instantaneously trained neural networks, spiking neuralnetworks, neocognition neural networks, dynamic neural networks,cascading neural networks, neuro-fuzzy neural networks, compositionalpattern-producing neural networks, memory neural networks, hierarchicaltemporal memory neural networks, deep feed forward neural networks,gated recurrent unit (GCU) neural networks, auto encoder neuralnetworks, variational auto encoder neural networks, de-noising autoencoder neural networks, sparse auto-encoder neural networks, Markovchain neural networks, restricted Boltzmann machine neural networks,deep belief neural networks, deep convolutional neural networks,de-convolutional neural networks, deep convolutional inverse graphicsneural networks, generative adversarial neural networks, liquid statemachine neural networks, extreme learning machine neural networks, echostate neural networks, deep residual neural networks, support vectormachine neural networks, neural Turing machine neural networks, and/orholographic associative memory neural networks, or hybrids orcombinations of the foregoing, or combinations with other expertsystems, such as rule-based systems, model-based systems (including onesbased on physical models, statistical models, flow-based models,biological models, biomimetic models, and the like).

In embodiments, FIGS. 5 through 31 depict exemplary neural networks andFIG. 4 depicts a legend showing the various components of the neuralnetworks depicted throughout FIGS. 5 to 31. FIG. 4 depicts variousneural net components depicted in cells that are assigned functions andrequirements. In embodiments, the various neural net examples mayinclude (from top to bottom in the example of FIG. 4): back feddata/sensor input cells, data/sensor input cells, noisy input cells, andhidden cells. The neural net components also include probabilistichidden cells, spiking hidden cells, output cells, match input/outputcells, recurrent cells, memory cells, different memory cells, kernels,and convolution or pool cells.

In embodiments, FIG. 5 depicts an exemplary perceptron neural networkthat may connect to, integrate with, or interface with the platform 100.The platform may also be associated with further neural net systems suchas a feed forward neural network (FIG. 6), a radial basis neural network(FIG. 7), a deep feed forward neural network (FIG. 8), a recurrentneural network (FIG. 9), a long/short term neural network (FIG. 10), anda gated recurrent neural network (FIG. 11). The platform may also beassociated with further neural net systems such as an auto encoderneural network (FIG. 12), a variational neural network (FIG. 13), adenoising neural network (FIG. 14), a sparse neural network (FIG. 15), aMarkov chain neural network (FIG. 16), and a Hopfield network neuralnetwork (FIG. 17). The platform may further be associated withadditional neural net systems such as a Boltzmann machine neural network(FIG. 18), a restricted BM neural network (FIG. 19), a deep beliefneural network (FIG. 20), a deep convolutional neural network (FIG. 21),a deconvolutional neural network (FIG. 22), and a deep convolutionalinverse graphics neural network (FIG. 23). The platform may also beassociated with further neural net systems such as a generativeadversarial neural network (FIG. 24), a liquid state machine neuralnetwork (FIG. 25), an extreme learning machine neural network (FIG. 26),an echo state neural network (FIG. 27), a deep residual neural network(FIG. 28), a Kohonen neural network (FIG. 29), a support vector machineneural network (FIG. 30), and a neural Turing machine neural network(FIG. 31).

The foregoing neural networks may have a variety of nodes or neurons,which may perform a variety of functions on inputs, such as inputsreceived from sensors or other data sources, including other nodes.Functions may involve weights, features, feature vectors, and the like.Neurons may include perceptrons, neurons that mimic biological functions(such as of the human senses of touch, vision, taste, hearing, andsmell), and the like. Continuous neurons, such as with sigmoidalactivation, may be used in the context of various forms of neural net,such as where back propagation is involved.

In many embodiments, an expert system or neural network may be trained,such as by a human operator or supervisor, or based on a data set,model, or the like. Training may include presenting the neural networkwith one or more training data sets that represent values, such assensor data, event data, parameter data, and other types of data(including the many types described throughout this disclosure), as wellas one or more indicators of an outcome, such as an outcome of aprocess, an outcome of a calculation, an outcome of an event, an outcomeof an activity, or the like. Training may include training inoptimization, such as training a neural network to optimize one or moresystems based on one or more optimization approaches, such as Bayesianapproaches, parametric Bayes classifier approaches, k-nearest-neighborclassifier approaches, iterative approaches, interpolation approaches,Pareto optimization approaches, algorithmic approaches, and the like.Feedback may be provided in a process of variation and selection, suchas with a genetic algorithm that evolves one or more solutions based onfeedback through a series of rounds.

In embodiments, a plurality of neural networks may be deployed in acloud platform that receives data streams and other inputs collected(such as by mobile data collectors) in one or more transactionalenvironments and transmitted to the cloud platform over one or morenetworks, including using network coding to provide efficienttransmission. In the cloud platform, optionally using massively parallelcomputational capability, a plurality of different neural networks ofvarious types (including modular forms, structure-adaptive forms,hybrids, and the like) may be used to undertake prediction,classification, control functions, and provide other outputs asdescribed in connection with expert systems disclosed throughout thisdisclosure. The different neural networks may be structured to competewith each other (optionally including use evolutionary algorithms,genetic algorithms, or the like), such that an appropriate type ofneural network, with appropriate input sets, weights, node types andfunctions, and the like, may be selected, such as by an expert system,for a specific task involved in a given context, workflow, environmentprocess, system, or the like.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a feed forwardneural network, which moves information in one direction, such as from adata input, like a data source related to at least one resource orparameter related to a transactional environment, such as any of thedata sources mentioned throughout this disclosure, through a series ofneurons or nodes, to an output. Data may move from the input nodes tothe output nodes, optionally passing through one or more hidden nodes,without loops. In embodiments, feed forward neural networks may beconstructed with various types of units, such as binary McCulloch-Pittsneurons, the simplest of which is a perceptron.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a capsule neuralnetwork, such as for prediction, classification, or control functionswith respect to a transactional environment, such as relating to one ormore of the machines and automated systems described throughout thisdisclosure.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a radial basisfunction (RBF) neural network, which may be preferred in some situationsinvolving interpolation in a multi-dimensional space (such as whereinterpolation is helpful in optimizing a multi-dimensional function,such as for optimizing a data marketplace as described here, optimizingthe efficiency or output of a power generation system, a factory system,or the like, or other situation involving multiple dimensions. Inembodiments, each neuron in the RBF neural network stores an examplefrom a training set as a “prototype.” Linearity involved in thefunctioning of this neural network offers RBF the advantage of nottypically suffering from problems with local minima or maxima.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a radial basisfunction (RBF) neural network, such as one that employs a distancecriterion with respect to a center (e.g., a Gaussian function). A radialbasis function may be applied as a replacement for a hidden layer, suchas a sigmoidal hidden layer transfer, in a multi-layer perceptron. AnRBF network may have two layers, such as where an input is mapped ontoeach RBF in a hidden layer. In embodiments, an output layer may comprisea linear combination of hidden layer values representing, for example, amean predicted output. The output layer value may provide an output thatis the same as or similar to that of a regression model in statistics.In classification problems, the output layer may be a sigmoid functionof a linear combination of hidden layer values, representing a posteriorprobability. Performance in both cases is often improved by shrinkagetechniques, such as ridge regression in classical statistics. Thiscorresponds to a prior belief in small parameter values (and thereforesmooth output functions) in a Bayesian framework. RBF networks may avoidlocal minima, because the only parameters that are adjusted in thelearning process are the linear mapping from hidden layer to outputlayer. Linearity ensures that the error surface is quadratic andtherefore has a single minimum. In regression problems, this may befound in one matrix operation. In classification problems, the fixednon-linearity introduced by the sigmoid output function may be handledusing an iteratively re-weighted least squares function or the like. RBFnetworks may use kernel methods such as support vector machines (SVM)and Gaussian processes (where the RBF is the kernel function). Anon-linear kernel function may be used to project the input data into aspace where the learning problem may be solved using a linear model.

In embodiments, an RBF neural network may include an input layer, ahidden layer, and a summation layer. In the input layer, one neuronappears in the input layer for each predictor variable. In the case ofcategorical variables, N−1 neurons are used, where N is the number ofcategories. The input neurons may, in embodiments, standardize the valueranges by subtracting the median and dividing by the interquartilerange. The input neurons may then feed the values to each of the neuronsin the hidden layer. In the hidden layer, a variable number of neuronsmay be used (determined by the training process). Each neuron mayconsist of a radial basis function that is centered on a point with asmany dimensions as a number of predictor variables. The spread (e.g.,radius) of the RBF function may be different for each dimension. Thecenters and spreads may be determined by training. When presented withthe vector of input values from the input layer, a hidden neuron maycompute a Euclidean distance of the test case from the neuron's centerpoint and then apply the RBF kernel function to this distance, such asusing the spread values. The resulting value may then be passed to thesummation layer. In the summation layer, the value coming out of aneuron in the hidden layer may be multiplied by a weight associated withthe neuron and may add to the weighted values of other neurons. This sumbecomes the output. For classification problems, one output is produced(with a separate set of weights and summation units) for each targetcategory. The value output for a category is the probability that thecase being evaluated has that category. In training of an RBF, variousparameters may be determined, such as the number of neurons in a hiddenlayer, the coordinates of the center of each hidden-layer function, thespread of each function in each dimension, and the weights applied tooutputs as they pass to the summation layer. Training may be used byclustering algorithms (such as k-means clustering), by evolutionaryapproaches, and the like.

In embodiments, a recurrent neural network may have a time-varying,real-valued (more than just zero or one) activation (output). Eachconnection may have a modifiable real-valued weight. Some of the nodesare called labeled nodes, some output nodes, and others hidden nodes.For supervised learning in discrete time settings, training sequences ofreal-valued input vectors may become sequences of activations of theinput nodes, one input vector at a time. At each time step, eachnon-input unit may compute its current activation as a nonlinearfunction of the weighted sum of the activations of all units from whichit receives connections. The system may explicitly activate (independentof incoming signals) some output units at certain time steps.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a self-organizingneural network, such as a Kohonen self-organizing neural network, suchas for visualization of views of data, such as low-dimensional views ofhigh-dimensional data. The self-organizing neural network may applycompetitive learning to a set of input data, such as from one or moresensors or other data inputs from or associated with a transactionalenvironment, including any machine or component that relates to thetransactional environment. In embodiments, the self-organizing neuralnetwork may be used to identify structures in data, such as unlabeleddata, such as in data sensed from a range of data sources about orsensors in or about in a transactional environment, where sources of thedata are unknown (such as where events may be coming from any of a rangeof unknown sources). The self-organizing neural network may organizestructures or patterns in the data, such that they may be recognized,analyzed, and labeled, such as identifying market behavior structures ascorresponding to other events and signals.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a recurrent neuralnetwork, which may allow for a bi-directional flow of data, such aswhere connected units (e.g., neurons or nodes) form a directed cycle.Such a network may be used to model or exhibit dynamic temporalbehavior, such as involved in dynamic systems, such as a wide variety ofthe automation systems, machines and devices described throughout thisdisclosure, such as an automated agent interacting with a marketplacefor purposes of collecting data, testing spot market transactions,execution transactions, and the like, where dynamic system behaviorinvolves complex interactions that a user may desire to understand,predict, control and/or optimize. For example, the recurrent neuralnetwork may be used to anticipate the state of a market, such as oneinvolving a dynamic process or action, such as a change in state of aresource that is traded in or that enables a marketplace oftransactional environment. In embodiments, the recurrent neural networkmay use internal memory to process a sequence of inputs, such as fromother nodes and/or from sensors and other data inputs from or about thetransactional environment, of the various types described herein. Inembodiments, the recurrent neural network may also be used for patternrecognition, such as for recognizing a machine, component, agent, orother item based on a behavioral signature, a profile, a set of featurevectors (such as in an audio file or image), or the like. In anon-limiting example, a recurrent neural network may recognize a shiftin an operational mode of a marketplace or machine by learning toclassify the shift from a training data set consisting of a stream ofdata from one or more data sources of sensors applied to or about one ormore resources.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a modular neuralnetwork, which may comprise a series of independent neural networks(such as ones of various types described herein) that are moderated byan intermediary. Each of the independent neural networks in the modularneural network may work with separate inputs, accomplishing subtasksthat make up the task the modular network as whole is intended toperform. For example, a modular neural network may comprise a recurrentneural network for pattern recognition, such as to recognize what typeof machine or system is being sensed by one or more sensors that areprovided as input channels to the modular network and an RBF neuralnetwork for optimizing the behavior of the machine or system onceunderstood. The intermediary may accept inputs of each of the individualneural networks, process them, and create output for the modular neuralnetwork, such an appropriate control parameter, a prediction of state,or the like.

Combinations among any of the pairs, triplets, or larger combinations,of the various neural network types described herein, are encompassed bythe present disclosure. This may include combinations where an expertsystem uses one neural network for recognizing a pattern (e.g., apattern indicating a problem or fault condition) and a different neuralnetwork for self-organizing an activity or work flow based on therecognized pattern (such as providing an output governing autonomouscontrol of a system in response to the recognized condition or pattern).This may also include combinations where an expert system uses oneneural network for classifying an item (e.g., identifying a machine, acomponent, or an operational mode) and a different neural network forpredicting a state of the item (e.g., a fault state, an operationalstate, an anticipated state, a maintenance state, or the like). Modularneural networks may also include situations where an expert system usesone neural network for determining a state or context (such as a stateof a machine, a process, a work flow, a marketplace, a storage system, anetwork, a data collector, or the like) and a different neural networkfor self-organizing a process involving the state or context (e.g., adata storage process, a network coding process, a network selectionprocess, a data marketplace process, a power generation process, amanufacturing process, a refining process, a digging process, a boringprocess, or other process described herein).

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a physical neuralnetwork where one or more hardware elements is used to perform orsimulate neural behavior. In embodiments, one or more hardware neuronsmay be configured to stream voltage values, current values, or the likethat represent sensor data, such as to calculate information from analogsensor inputs representing energy consumption, energy production, or thelike, such as by one or more machines providing energy or consumingenergy for one or more transactions. One or more hardware nodes may beconfigured to stream output data resulting from the activity of theneural net. Hardware nodes, which may comprise one or more chips,microprocessors, integrated circuits, programmable logic controllers,application-specific integrated circuits, field-programmable gatearrays, or the like, may be provided to optimize the machine that isproducing or consuming energy, or to optimize another parameter of somepart of a neural net of any of the types described herein. Hardwarenodes may include hardware for acceleration of calculations (such asdedicated processors for performing basic or more sophisticatedcalculations on input data to provide outputs, dedicated processors forfiltering or compressing data, dedicated processors for de-compressingdata, dedicated processors for compression of specific file or datatypes (e.g., for handling image data, video streams, acoustic signals,thermal images, heat maps, or the like), and the like. A physical neuralnetwork may be embodied in a data collector, including one that may bereconfigured by switching or routing inputs in varying configurations,such as to provide different neural net configurations within the datacollector for handling different types of inputs (with the switching andconfiguration optionally under control of an expert system, which mayinclude a software-based neural net located on the data collector orremotely). A physical, or at least partially physical, neural networkmay include physical hardware nodes located in a storage system, such asfor storing data within a machine, a data storage system, a distributedledger, a mobile device, a server, a cloud resource, or in atransactional environment, such as for accelerating input/outputfunctions to one or more storage elements that supply data to or takedata from the neural net. A physical, or at least partially physical,neural network may include physical hardware nodes located in a network,such as for transmitting data within, to or from an industrialenvironment, such as for accelerating input/output functions to one ormore network nodes in the net, accelerating relay functions, or thelike. In embodiments of a physical neural network, an electricallyadjustable resistance material may be used for emulating the function ofa neural synapse. In embodiments, the physical hardware emulates theneurons, and software emulates the neural network between the neurons.In embodiments, neural networks complement conventional algorithmiccomputers. They are versatile and may be trained to perform appropriatefunctions without the need for any instructions, such as classificationfunctions, optimization functions, pattern recognition functions,control functions, selection functions, evolution functions, and others.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a multilayeredfeed forward neural network, such as for complex pattern classificationof one or more items, phenomena, modes, states, or the like. Inembodiments, a multilayered feed forward neural network may be trainedby an optimization technique, such as a genetic algorithm, such as toexplore a large and complex space of options to find an optimum, ornear-optimum, global solution. For example, one or more geneticalgorithms may be used to train a multilayered feed forward neuralnetwork to classify complex phenomena, such as to recognize complexoperational modes of machines, such as modes involving complexinteractions among machines (including interference effects, resonanceeffects, and the like), modes involving non-linear phenomena, modesinvolving critical faults, such as where multiple, simultaneous faultsoccur, making root cause analysis difficult, and others. In embodiments,a multilayered feed forward neural network may be used to classifyresults from monitoring of a marketplace, such as monitoring systems,such as automated agents, that operate within the marketplace, as wellas monitoring resources that enable the marketplace, such as computing,networking, energy, data storage, energy storage, and other resources.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a feed-forward,back-propagation multi-layer perceptron (MLP) neural network, such asfor handling one or more remote sensing applications, such as for takinginputs from sensors distributed throughout various transactionalenvironments. In embodiments, the MLP neural network may be used forclassification of transactional environments and resource environments,such as spot markets, forward markets, energy markets, renewable energycredit (REC) markets, networking markets, advertising markets, spectrummarkets, ticketing markets, rewards markets, compute markets, and othersmentioned throughout this disclosure, as well as physical resources andenvironments that produce them, such as energy resources (includingrenewable energy environments, mining environments, explorationenvironments, drilling environments, and the like, includingclassification of geological structures (including underground featuresand above ground features), classification of materials (includingfluids, minerals, metals, and the like), and other problems. This mayinclude fuzzy classification.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use astructure-adaptive neural network, where the structure of a neuralnetwork is adapted, such as based on a rule, a sensed condition, acontextual parameter, or the like. For example, if a neural network doesnot converge on a solution, such as classifying an item or arriving at aprediction, when acting on a set of inputs after some amount oftraining, the neural network may be modified, such as from a feedforward neural network to a recurrent neural network, such as byswitching data paths between some subset of nodes from unidirectional tobi-directional data paths. The structure adaptation may occur undercontrol of an expert system, such as to trigger adaptation uponoccurrence of a trigger, rule or event, such as recognizing occurrenceof a threshold (such as an absence of a convergence to a solution withina given amount of time) or recognizing a phenomenon as requiringdifferent or additional structure (such as recognizing that a system isvarying dynamically or in a non-linear fashion). In one non-limitingexample, an expert system may switch from a simple neural networkstructure like a feed forward neural network to a more complex neuralnetwork structure like a recurrent neural network, a convolutionalneural network, or the like upon receiving an indication that acontinuously variable transmission is being used to drive a generator,turbine, or the like in a system being analyzed.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an autoencoder,autoassociator or Diabolo neural network, which may be similar to amultilayer perceptron (MLP) neural network, such as where there may bean input layer, an output layer and one or more hidden layers connectingthem. However, the output layer in the auto-encoder may have the samenumber of units as the input layer, where the purpose of the MLP neuralnetwork is to reconstruct its own inputs (rather than just emitting atarget value). Therefore, the auto encoders are may operate as anunsupervised learning model. An auto encoder may be used, for example,for unsupervised learning of efficient codings, such as fordimensionality reduction, for learning generative models of data, andthe like. In embodiments, an auto-encoding neural network may be used toself-learn an efficient network coding for transmission of analog sensordata from a machine over one or more networks or of digital data fromone or more data sources. In embodiments, an auto-encoding neuralnetwork may be used to self-learn an efficient storage approach forstorage of streams of data.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a probabilisticneural network (PNN), which, in embodiments, may comprise a multi-layer(e.g., four-layer) feed forward neural network, where layers may includeinput layers, hidden layers, pattern/summation layers and an outputlayer. In an embodiment of a PNN algorithm, a parent probabilitydistribution function (PDF) of each class may be approximated, such asby a Parzen window and/or a non-parametric function. Then, using the PDFof each class, the class probability of a new input is estimated, andBayes' rule may be employed, such as to allocate it to the class withthe highest posterior probability. A PNN may embody a Bayesian networkand may use a statistical algorithm or analytic technique, such asKernel Fisher discriminant analysis technique. The PNN may be used forclassification and pattern recognition in any of a wide range ofembodiments disclosed herein. In one non-limiting example, aprobabilistic neural network may be used to predict a fault condition ofan engine based on collection of data inputs from sensors andinstruments for the engine.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a time delayneural network (TDNN), which may comprise a feed forward architecturefor sequential data that recognizes features independent of sequenceposition. In embodiments, to account for time shifts in data, delays areadded to one or more inputs, or between one or more nodes, so thatmultiple data points (from distinct points in time) are analyzedtogether. A time delay neural network may form part of a larger patternrecognition system, such as using a perceptron network. In embodiments,a TDNN may be trained with supervised learning, such as where connectionweights are trained with back propagation or under feedback. Inembodiments, a TDNN may be used to process sensor data from distinctstreams, such as a stream of velocity data, a stream of accelerationdata, a stream of temperature data, a stream of pressure data, and thelike, where time delays are used to align the data streams in time, suchas to help understand patterns that involve understanding of the variousstreams (e.g., changes in price patterns in spot or forward markets).

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a convolutionalneural network (referred to in some cases as a CNN, a ConvNet, a shiftinvariant neural network, or a space invariant neural network), whereinthe units are connected in a pattern similar to the visual cortex of thehuman brain. Neurons may respond to stimuli in a restricted region ofspace, referred to as a receptive field. Receptive fields may partiallyoverlap, such that they collectively cover the entire (e.g., visual)field. Node responses may be calculated mathematically, such as by aconvolution operation, such as using multilayer perceptrons that useminimal preprocessing. A convolutional neural network may be used forrecognition within images and video streams, such as for recognizing atype of machine in a large environment using a camera system disposed ona mobile data collector, such as on a drone or mobile robot. Inembodiments, a convolutional neural network may be used to provide arecommendation based on data inputs, including sensor inputs and othercontextual information, such as recommending a route for a mobile datacollector. In embodiments, a convolutional neural network may be usedfor processing inputs, such as for natural language processing ofinstructions provided by one or more parties involved in a workflow inan environment. In embodiments, a convolutional neural network may bedeployed with a large number of neurons (e.g., 100,000, 500,000 ormore), with multiple (e.g., 4, 5, 6 or more) layers, and with many(e.g., millions) of parameters. A convolutional neural net may use oneor more convolutional nets.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a regulatoryfeedback network, such as for recognizing emergent phenomena (such asnew types of behavior not previously understood in a transactionalenvironment).

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a self-organizingmap (SOM), involving unsupervised learning. A set of neurons may learnto map points in an input space to coordinates in an output space. Theinput space may have different dimensions and topology from the outputspace, and the SOM may preserve these while mapping phenomena intogroups.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a learning vectorquantization neural net (LVQ). Prototypical representatives of theclasses may parameterize, together with an appropriate distance measure,in a distance-based classification scheme.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an echo statenetwork (ESN), which may comprise a recurrent neural network with asparsely connected, random hidden layer. The weights of output neuronsmay be changed (e.g., the weights may be trained based on feedback). Inembodiments, an ESN may be used to handle time series patterns, such as,in an example, recognizing a pattern of events associated with a market,such as the pattern of price changes in response to stimuli.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a Bi-directional,recurrent neural network (BRNN), such as using a finite sequence ofvalues (e.g., voltage values from a sensor) to predict or label eachelement of the sequence based on both the past and the future context ofthe element. This may be done by adding the outputs of two RNNs, such asone processing the sequence from left to right, the other one from rightto left. The combined outputs are the predictions of target signals,such as ones provided by a teacher or supervisor. A bi-directional RNNmay be combined with a long short-term memory RNN.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a hierarchical RNNthat connects elements in various ways to decompose hierarchicalbehavior, such as into useful subprograms. In embodiments, ahierarchical RNN may be used to manage one or more hierarchicaltemplates for data collection in a transactional environment.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a stochasticneural network, which may introduce random variations into the network.Such random variations may be viewed as a form of statistical sampling,such as Monte Carlo sampling.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a genetic scalerecurrent neural network. In such embodiments, an RNN (often an LSTM) isused where a series is decomposed into a number of scales where everyscale informs the primary length between two consecutive points. A firstorder scale consists of a normal RNN, a second order consists of allpoints separated by two indices and so on. The Nth order RNN connectsthe first and last node. The outputs from all the various scales may betreated as a committee of members, and the associated scores may be usedgenetically for the next iteration.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a committee ofmachines (CoM), comprising a collection of different neural networksthat together “vote” on a given example. Because neural networks maysuffer from local minima, starting with the same architecture andtraining, but using randomly different initial weights often givesdifferent results. A CoM tends to stabilize the result.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an associativeneural network (ASNN), such as involving an extension of a committee ofmachines that combines multiple feed forward neural networks and ak-nearest neighbor technique. It may use the correlation betweenensemble responses as a measure of distance amid the analyzed cases forthe kNN. This corrects the bias of the neural network ensemble. Anassociative neural network may have a memory that may coincide with atraining set. If new data become available, the network instantlyimproves its predictive ability and provides data approximation(self-learns) without retraining. Another important feature of ASNN isthe possibility to interpret neural network results by analysis ofcorrelations between data cases in the space of models.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an instantaneouslytrained neural network (ITNN), where the weights of the hidden and theoutput layers are mapped directly from training vector data.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a spiking neuralnetwork, which may explicitly consider the timing of inputs. The networkinput and output may be represented as a series of spikes (such as adelta function or more complex shapes). SNNs may process information inthe time domain (e.g., signals that vary over time, such as signalsinvolving dynamic behavior of markets or transactional environments).They are often implemented as recurrent networks.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a dynamic neuralnetwork that addresses nonlinear multivariate behavior and includeslearning of time-dependent behavior, such as transient phenomena anddelay effects. Transients may include behavior of shifting marketvariables, such as prices, available quantities, availablecounterparties, and the like.

In embodiments, cascade correlation may be used as an architecture andsupervised learning algorithm, supplementing adjustment of the weightsin a network of fixed topology. Cascade-correlation may begin with aminimal network, then automatically trains and add new hidden units oneby one, creating a multi-layer structure. Once a new hidden unit hasbeen added to the network, its input-side weights may be frozen. Thisunit then becomes a permanent feature-detector in the network, availablefor producing outputs or for creating other, more complex featuredetectors. The cascade-correlation architecture may learn quickly,determine its own size and topology, and retain the structures it hasbuilt even if the training set changes and requires no back-propagation.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a neuro-fuzzynetwork, such as involving a fuzzy inference system in the body of anartificial neural network. Depending on the type, several layers maysimulate the processes involved in a fuzzy inference, such asfuzzification, inference, aggregation and defuzzification. Embedding afuzzy system in a general structure of a neural net as the benefit ofusing available training methods to find the parameters of a fuzzysystem.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a compositionalpattern-producing network (CPPN), such as a variation of an associativeneural network (ANN) that differs the set of activation functions andhow they are applied. While typical ANNs often contain only sigmoidfunctions (and sometimes Gaussian functions), CPPNs may include bothtypes of functions and many others. Furthermore, CPPNs may be appliedacross the entire space of possible inputs, so that they may represent acomplete image. Since they are compositions of functions, CPPNs ineffect encode images at infinite resolution and may be sampled for aparticular display at whatever resolution is optimal.

This type of network may add new patterns without re-training. Inembodiments, methods and systems described herein that involve an expertsystem or self-organization capability may use a one-shot associativememory network, such as by creating a specific memory structure, whichassigns each new pattern to an orthogonal plane using adjacentlyconnected hierarchical arrays.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a hierarchicaltemporal memory (HTM) neural network, such as involving the structuraland algorithmic properties of the neocortex. HTM may use a biomimeticmodel based on memory-prediction theory. HTM may be used to discover andinfer the high-level causes of observed input patterns and sequences.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a holographicassociative memory (HAM) neural network, which may comprise an analog,correlation-based, associative, stimulus-response system. Informationmay be mapped onto the phase orientation of complex numbers. The memoryis effective for associative memory tasks, generalization and patternrecognition with changeable attention.

In embodiments, various embodiments involving network coding may be usedto code transmission data among network nodes in a neural net, such aswhere nodes are located in one or more data collectors or machines in atransactional environment.

In embodiments, one or more of the controllers, circuits, systems, datacollectors, storage systems, network elements, or the like as describedthroughout this disclosure may be embodied in or on an integratedcircuit, such as an analog, digital, or mixed signal circuit, such as amicroprocessor, a programmable logic controller, an application-specificintegrated circuit, a field programmable gate array, or other circuit,such as embodied on one or more chips disposed on one or more circuitboards, such as to provide in hardware (with potentially acceleratedspeed, energy performance, input-output performance, or the like) one ormore of the functions described herein. This may include setting upcircuits with up to billions of logic gates, flip-flops, multi-plexers,and other circuits in a small space, facilitating high speed processing,low power dissipation, and reduced manufacturing cost compared withboard-level integration. In embodiments, a digital IC, typically amicroprocessor, digital signal processor, microcontroller, or the likemay use Boolean algebra to process digital signals to embody complexlogic, such as involved in the circuits, controllers, and other systemsdescribed herein. In embodiments, a data collector, an expert system, astorage system, or the like may be embodied as a digital integratedcircuit, such as a logic IC, memory chip, interface IC (e.g., a levelshifter, a serializer, a deserializer, and the like), a power managementIC and/or a programmable device; an analog integrated circuit, such as alinear IC, RF IC, or the like, or a mixed signal IC, such as a dataacquisition IC (including A/D converters, D/A converter, digitalpotentiometers) and/or a clock/timing IC.

With reference to FIG. 32, the environment includes an intelligentenergy and compute facility (such as a large scale facility hosting manycompute resources and having access to a large energy source, such as ahydropower source), as well as a host intelligent energy and computefacility resource management platform, referred to in some cases forconvenience as the energy and information technology platform (withnetworking, data storage, data processing and other resources asdescribed herein), a set of data sources, a set of expert systems,interfaces to a set of market platforms and external resources, and aset of user (or client) systems and devices.

A facility may be configured to access an inexpensive (at least duringsome time periods) power source (such as a hydropower dam, a wind farm,a solar array, a nuclear power plant, or a grid), to contain a large setof networked information technology resources, including processingunits, servers, and the like that are capable of flexible utilization(such as by switching inputs, switching configurations, switchingprogramming and the like), and to provide a range of outputs that canalso be flexibly configured (such as passing through power to a smartgrid, providing computational results (such as for cryptocurrencymining, artificial intelligence, or analytics). A facility may include apower storage system, such as for large scale storage of availablepower.

Example features and operations of an intelligent energy and computefacility resource management platform are described herein. Inoperation, a user can access the energy and information technologyplatform to initiate and manage a set of activities that involveoptimizing energy and computing resources among a diverse set ofavailable tasks. Energy resources may include hydropower, nuclear power,wind power, solar power, grid power and the like, as well as energystorage resources, such as batteries, gravity power, kinetic energystorage, pressurized fluids, and storage using chemical and/or thermaltechniques, such as energy storage in molten salts. Computing resourcesmay include GPUs, FPGAs, servers, chips, ASICs, processors, data storagemedia, networking resources, and many others. Available tasks mayinclude cryptocurrency hash processing, expert system processing,computer vision processing, NLP, path optimization, applications ofmodels such as for analytics, etc.

In embodiments, the platform may include various subsystems that may beimplemented as micro services, such that other subsystems of the systemaccess the functionality of a subsystem providing a micro service viaapplication programming interface API. In some embodiments, the variousservices that are provided by the subsystems may be deployed in bundlesthat are integrated, such as by a set of APIs. Examples of thesubsystems are described in greater detail with respect to FIG. 33.

The External Data Sources can include any system or device that canprovide data to the platform. Examples of external data sources caninclude market data sources (e.g., for financial markets, commercialmarkets (including e-commerce), advertising markets, energy markets,telecommunication markets, and many others), government or regulatorydata sources, industry specific data sources, subscription basedservices accessing proprietary or public information, and/or news datasources. The energy and computing resource platform accesses externaldata sources via a network (e.g., the Internet) in any suitable manner(e.g., crawlers, extract-transform-load (ETL) systems, gateways,brokers, application programming interfaces (APIs), spiders, distributeddatabase queries, and the like).

A facility, in the present example, is a facility that has an energyresource (e.g., a hydro power resource) and a set of compute resources(e.g., a set of flexible computing resources that can be provisioned andmanaged to perform computing tasks, such as GPUs, FPGAs and many others,a set of flexible networking resources that can similarly be provisionedand managed, such as by adjusting network coding protocols andparameters), and the like.

User and client systems and devices can include any system or devicethat may consume one or more computing or energy resource made availableby the energy and computing resource platform. Examples includecryptocurrency systems (e.g., for Bitcoin and other cryptocurrencymining operations), expert and artificial intelligence systems (such asneural networks and other systems, such as for computer vision, naturallanguage processing, path determination and optimization, patternrecognition, deep learning, supervised learning, decision support, andmany others), energy management systems (such as smart grid systems),and many others. User and client systems may include user devices, suchas smartphones, tablet computer devices, laptop computing devices,personal computing devices, smart televisions, gaming consoles, and thelike.

FIG. 33 illustrates an example energy and computing resource platformaccording to some embodiments of the present disclosure. In embodiments,the energy and computing resource platform may include a processingsystem 3302, a storage system 3304, and a communication system 3306.

The processing system 3302 may include one or more processors andmemory. The processors may operate in an individual or distributedmanner. The processors may be in the same physical device or in separatedevices, which may or may not be located in the same facility. Thememory may store computer-executable instructions that are executed bythe one or more processors. In embodiments, the processing system 3302may execute the facility management system 3308, the data acquisitionsystem 3310, the cognitive processing system 3312, the lead generationsystem 3314, the content generation system 3316, and the workflow system3318.

The storage system 3304 may include one or more computer-readablestorage mediums. The computer-readable storage mediums may be located inthe same physical device or in separate devices, which may or may not belocated in the same facility, which may or may not be located in thesame facility. The computer-readable storage mediums may include flashdevices, solid-state memory devices, hard disk drives, and the like. Inembodiments, the storage system 3304 stores one or more of a facilitydata store 3320, a person data store 3322, and/or include data storesfor any other type of data. The data stores are depicted separately forclarity of the description, but may be stored in the same or a distinctphysical location or device, and/or a given data store may bedistributed across physical locations or devices.

The communication system 3306 may include one or more transceiversand/or network devices that are configured to effectuate wireless orwired communication with one or more external devices, including userdevices and/or servers, via a network (e.g., the Internet and/or acellular network). In certain embodiments, the communication system 3306provides access to external data 3324, the internet, web-basedresources, a LAN, a WAN, and/or other systems or devices. Thecommunication system 3306 may implement any suitable communicationprotocol. For example, the communication system 3306 may implement anIEEE 801.11 wireless communication protocol and/or any suitable cellularcommunication protocol to effectuate wireless communication withexternal devices via a wireless network.

An example energy and computing resource management platform discovers,provisions, manages and optimizes energy and compute resources usingartificial intelligence and expert systems with sensitivity to marketand other conditions by learning on a set of outcomes. An example energyand computer resource management platform discovers and facilitatescataloging of resources, optionally by user entry and/or automateddetection (including peer detection). In certain embodiments, an energyand computing resource management platform implements a graphical userinterface to receive relevant information regarding the energy andcompute resources that are available. For example, a “digital twin” maybe created of an energy and compute facility that allows modeling,prediction and the like. In certain embodiments, an energy and computingresource management platform generates a set of data records that definethe facility or a set of facilities under common ownership or operationby a host. The data records may have any suitable schema. In someembodiments (e.g., FIG. 34), the facility data records may include afacility identifier (e.g., a unique identifier that corresponds to thefacility), a facility type (e.g., energy system and capabilities,compute systems and capabilities, networking systems and capabilities),facility attributes (e.g., name of the facility, name of the facilityinitiator, description of the facility, keywords of the facility, goalsof the facility, timing elements, schedules, and the like),participants/potential participants in the facility (e.g., identifiersof owners, operators, hosts, service providers, consumers, clients,users, workers, and others), and any suitable metadata (e.g., creationdate, launch date, scheduled requirements and the like). An exampleenergy and computer resource management platform generates content, suchas a document, message, alert, report, webpage and/or application pagebased on the contents of the data record. For example, an example energyand computer resource management platform obtains the data record of thefacility and populates a webpage template with the data (or selectedportions of the data) contained therein. In addition, an energy andcomputer resource management platform can implement management ofexisting facilities, update the data record of a facility, determine,predict, and/or estimate outcomes (e.g., energy produced, compute taskscompleted, processing outcomes achieved, financial outcomes achieved,service levels met and many others), and send of information (e.g.,updates, alerts, requests, instructions, and the like) to individualsand systems.

Data Acquisition Systems can acquire various types of data fromdifferent data sources and organize that data into one or more datastructures. In embodiments, the data acquisition system receives datafrom users via a user interface (e.g., user types in profileinformation). In embodiments, the data acquisition system can retrievedata from passive electronic sources and/or external data. Inembodiments, the data acquisition system can implement crawlers to crawldifferent websites or applications. In embodiments, the data acquisitionsystem can implement an API to retrieve data from external data sourcesor user devices (e.g., various contact lists from user's phone or emailaccount). In embodiments, the data acquisition system can structure theobtained data into appropriate data structures. In embodiments, the dataacquisition system generates and maintains person records based on datacollected regarding individuals. In embodiments, a person datastorestores person records. In some of these embodiments, the persondatastore may include one or more databases, indexes, tables, and thelike. Each person record may correspond to a respective individual andmay be organized according to any suitable schema.

FIG. 35 illustrates an example schema of a person record. In theexample, each person record may include a unique person identifier(e.g., username or value), and may define all data relating to a person,including a person's name, facilities they are a part of or associatedwith (e.g., a list of facility identifiers), attributes of the person(age, location, job, company, role, skills, competencies, capabilities,education history, job history, and the like), a list of contacts orrelationships of the person (e.g., in a role hierarchy or graph), andany suitable metadata (e.g., date joined, dates actions were taken,dates input was received, and the like).

In embodiments, the data acquisition system generates and maintains oneor more graphs based on the retrieved data. In some embodiments, a graphdatastore may store the one or more graphs. The graph may be specific toa facility or may be a global graph. The graph may be used in manydifferent applications (e.g., identifying a set of roles, such as forauthentication, for approvals, and the like for persons, or identifyingsystem configurations, capabilities, or the like, such as hierarchies ofenergy producing, computing, networking, or other systems, subsystemsand/or resources).

In embodiments, a graph may be stored in a graph database, where data isstored in a collection of nodes and edges. In some embodiments, a graphhas nodes representing entities and edges representing relationships,each node may have a node type (also referred to as an entity type) andan entity value, each edge may have a relationship type and may define arelationship between two entities. For example, a person node mayinclude a person ID that identifies the individual represented by thenode and a company node may include a company identifier that identifiesa company. A “works for” edge that is directed from a person node to acompany node may denote that the person represented by the edge nodeworks for the company represented by the company node. In anotherexample, a person node may include a person ID that identifies theindividual represented by the node and a facility node may include afacility identifier that identifies a facility. A “manages” edge that isdirected from a person node to a facility node may denote that theperson represented by the person node is a manager of the facilityrepresented by the facility node. Furthermore in embodiments, an edge ornode may contain or reference additional data. For example, a “manages”edge may include a function that indicates a specific function within afacility that is managed by a person. The graph(s) can be used in anumber of different applications, which are discussed with respect tothe cognitive processing system.

In embodiments, validated Identity information may be imported from oneor more identity information providers, as well as data from LinkedIn™and other social network sources regarding data acquisition andstructuring data. In embodiments, the data acquisition system mayinclude an identity management system (not shown in Figs) of theplatform may manage identity stitching, identity resolution, identitynormalization, and the like, such as determining where an individualrepresented across different social networking sites and email contactsis in fact the same person. In embodiments, the data acquisition systemmay include a profile aggregation system (not shown in Figs) that findsand aggregates disparate pieces of information to generate acomprehensive profile for a person. The profile aggregation system mayalso deduplicate individuals.

The cognitive processing system may implement one or more of machinelearning processes, artificial intelligence processes, analyticsprocesses, natural language processing processes, and natural languagegeneration processes. FIG. 36 illustrates an example cognitiveprocessing system 3312 according to some embodiments of the presentdisclosure. In this example, the cognitive processing system may includea machine learning system 3602, an artificial intelligence (AI) system3604, an analytics system 3606, a natural language processing system3608, and a natural language generation system 3610.

In embodiments, the machine learning system 3602 may train models, suchas predictive models (e.g., various types of neural networks, regressionbased models, and other machine-learned models). In embodiments,training can be supervised, semi-supervised, or unsupervised. Inembodiments, training can be done using training data, which may becollected or generated for training purposes.

An example machine learning system 3602 trains a facility output model.A facility output model (or prediction model) may be a model thatreceive facility attributes and outputs one or more predictionsregarding the production or other output of a facility. Examples ofpredictions may be the amount of energy a facility will produce, theamount of processing the facility will undertake, the amount of data anetwork will be able to transfer, the amount of data that can be stored,the price of a component, service or the like (such as supplied to orprovided by a facility), a profit generated by accomplishing a giventasks, the cost entailed in performing an action, and the like. In eachcase, the machine learning system optionally trains a model based ontraining data. In embodiments, the machine learning system may receivevectors containing facility attributes (e.g., facility type, facilitycapability, objectives sought, constraints or rules that apply toutilization of resources or the facility, or the like), personattributes (e.g., role, components managed, and the like), and outcomes(e.g., energy produced, computing tasks completed, and financialresults, among many others). Each vector corresponds to a respectiveoutcome and the attributes of the respective facility and respectiveactions that led to the outcome. The machine learning system takes inthe vectors and generates predictive model based thereon. Inembodiments, the machine learning system may store the predictive modelsin the model datastore.

In embodiments, training can also be done based on feedback received bythe system, which is also referred to as “reinforcement learning.” Inembodiments, the machine learning system may receive a set ofcircumstances that led to a prediction (e.g., attributes of facility,attributes of a model, and the like) and an outcome related to thefacility and may update the model according to the feedback.

In embodiments, training may be provided from a training data set thatis created by observing actions of a set of humans, such as facilitymanagers managing facilities that have various capabilities and that areinvolved in various contexts and situations. This may include use ofrobotic process automation to learn on a training data set ofinteractions of humans with interfaces, such as graphical userinterfaces, of one or more computer programs, such as dashboards,control systems, and other systems that are used to manage an energy andcompute management facility.

In embodiments, an artificial intelligence (AI) system leveragespredictive models to make predictions regarding facilities. Examples ofpredictions include ones related to inputs to a facility (e.g.,available energy, cost of energy, cost of compute resources, networkingcapacity and the like, as well as various market information, such aspricing information for end use markets), ones related to components orsystems of a facility (including performance predictions, maintenancepredictions, uptime/downtime predictions, capacity predictions and thelike), ones related to functions or workflows of the facility (such asones that involved conditions or states that may result in following oneor more distinct possible paths within a workflow, a process, or thelike), ones related to outputs of the facility, and others. Inembodiments, the AI system receives a facility identifier. In responseto the facility identifier, the AI system may retrieve attributescorresponding to the facility. In some embodiments, the AI system mayobtain the facility attributes from a graph. Additionally oralternatively, the AI system may obtain the facility attributes from afacility record corresponding to the facility identifier, and the personattributes from a person record corresponding to the person identifier.

Examples of additional attributes that can be used to make predictionsabout a facility or a related process of system include: relatedfacility information; owner goals (including financial goals); clientgoals; and many more additional or alternative attributes. Inembodiments, the AI system may output scores for each possibleprediction, where each prediction corresponds to a possible outcome. Forexample, in using a prediction model used to determine a likelihood thata hydroelectric source for a facility will produce 5 MW of power, theprediction model can output a score for a “will produce” outcome and ascore for a “will not produce” outcome. The AI system may then selectthe outcome with the highest score as the prediction. Alternatively, theAI system may output the respective scores to a requesting system.

In embodiments, a clustering system clusters records or entities basedon attributes contained herein. For example, similar facilities,resources, people, clients, or the like may be clustered. The clusteringsystem may implement any suitable clustering algorithm. For example,when clustering people records to identify a list of customer leadscorresponding to resources that can be sold by a facility, theclustering system may implement k-nearest neighbors clustering, wherebythe clustering system identifies k people records that most closelyrelate to the attributes defined for the facility. In another example,the clustering system may implement k-means clustering, such that theclustering system identifies k different clusters of people records,whereby the clustering system or another system selects items from thecluster.

In embodiments, an analytics system may perform analytics relating tovarious aspects of the energy and computing resource platform. Theanalytics system may analyze certain communications to determine whichconfigurations of a facility produce the greatest yield, what conditionstend to indicate potential faults or problems, and the like.

FIG. 37 depicts example operations of a lead generation system 3314 togenerate a lead list. Lead generation system 3314 receives 3702 a listof potential leads (such as for consumers of available products orresources). The lead generation system 3314 may provide 3704 the list ofleads to the clustering system. The clustering system clusters 3706 theprofile of the lead with the clusters of facility attributes to identifyone or more clusters. In embodiments, the clustering system returns 3708a list of lead prospects. In other embodiments, the clustering systemreturns 3708 the clusters, and the lead generation system selects 3710the list of leads from the cluster to which a prospect belongs.

FIG. 38 depicts example operations of a lead generation system 3314 todetermine facility outputs for leads identified in the list of leads. Inembodiments, the lead generation system 3314 provides 3802 a leadidentifier of a respective lead to the AI system 3604. The AI system maythen obtain the lead attributes of the lead and facility attributes ofthe facility and may feed 3804 the respective attributes into aprediction model. The prediction model returns 3806 a prediction, whichmay be scores associated with each possible outcome, or a singlepredicted outcome that was selected based on its respective score (e.g.,the outcome having the highest score). Based on the prediction score oroutcome, the lead may be stored and/or categorized (e.g. operation3808). The lead generation system may iterate (e.g., operation 3810) inthis manner for each lead in the lead list. For example, the leadgeneration system may generate leads (e.g., operation 3812) that areconsumers of compute capabilities, energy capabilities, predictions andforecasts, optimization results, and others.

In embodiments the lead generation system provides the facility operatoror host of the systems with an indicator of the reason why a lead may bewilling to engage the facility, such as, for example, that the lead isan intensive user of computing resources, such as to forecast behaviorof a complex, multi-variable market, or to mine for cryptocurrency.

In embodiments, a content generation system of the platform generatescontent for a contact event, such as an email, text message, or a postto a network, or a machine-to-machine message, such as communicating viaan API or a peer-to-peer system. In embodiments, the content iscustomized using artificial intelligence based on the attributes of thefacility, attributes of a recipient (e.g., based on the profile of aperson, the role of a person, or the like), and/or relating to theproject or activity to which the facility relates. The contentgeneration system may be seeded with a set of templates, which may becustomized, such as by training the content generation system on atraining set of data created by human writers, and which may be furthertrained by feedback based on outcomes tracked by the platform, such asoutcomes indicating success of particular forms of communication ingenerating donations to a facility, as well as other indicators as notedthroughout this disclosure. The content generation system may customizecontent based on attributes of the facility, a project, and/or one ormore people, and the like. For example, a facility manager may receiveshort messages about events related to facility operations, includingcodes, acronyms and jargon, while an outside consumer of outputs fromthe facility may receive a more formal report relating to the sameevent.

FIG. 39 depicts example operations of a content generation system 3316to generate personalized content. The content generation system receives3902 a recipient id, a sender id (which may be a person or a system,among others), and a facility id. The content generation system maydetermine 3904 the appropriate content template to use based on therelationships among the recipient, sender and facility and/or based onother considerations (e.g., a recipient who is a busy manager is morelikely to respond to less formal messages or more formal messages). Thecontent generation system may provide the template (or an identifierthereof) to the natural language generation system, along with therecipient id, the sender id, and the facility id. The natural languagegeneration system may obtain 3906 facility attributes based on thefacility id, and person attributes corresponding to the recipient orsender based on their identities. The natural language generation systemmay then generate 3908 the personalized or customized content based onthe selected template, the facility parameters, and/or other attributesof the various types described herein. The natural language generationsystem may output 3910 the generated content to the content generationsystem.

In embodiments, a person, such as a facility manager, may approve thegenerated content provided by the content generation system and/or makeedits to the generated content, then send the content, such as via emailand/or other channels. In embodiments, the platform tracks the contactevent.

In embodiments, the workflow management system may support variousworkflows associated with a facility, such as including interfaces ofthe platform by which a facility manager may review various analyticresults, status information, and the like. In embodiments, the workflowmanagement system tracks the operation of a post-action follow-up moduleto ensure that the correct follow-up messages are automatically, orunder control of a facility agent using the platform, sent toappropriate individuals, systems and/or services.

In the various embodiments, various elements are included for a workflowfor each of an energy project, a compute project (e.g., cryptocurrencyand/or AI) and hybrids.

Transactions, as described herein, may include financial transactionsusing various forms of currency, including fiat currencies supported bygovernments, cryptocurrencies, tokens or points (such as loyalty pointsand rewards points offered by airlines, hospitality providers, and manyother businesses), and the like. Transactions may also be understood toencompass a wide range of other transactions involving exchanges ofvalue, including in-kind transactions that involve the exchange ofresources. Transactions may include exchanges of currencies of varioustypes, including exchanges between currencies and in-kind resources.Resources exchanged may include goods, services, compute resources,energy resources, network bandwidth resources, natural resources, andthe like. Transactions may also include ones involving attentionresources, such as by prospective counterparties in transactions, suchas consumers of goods, services and other, who may be humans or, in somesituations, may be other consumers, such as intelligent (e.g., AI-basedagents).

Certain features of the present disclosure are referenced as a computetask herein. The term compute task should be understood broadly. Incertain embodiments, and without limitation to any other aspect of thepresent disclosure, a compute task includes any one or more of:execution of one or more computer readable instructions by a processor;intermediate storage of commands for execution (e.g., in a cache orbuffer); operations to store, communicate, or perform calculations ondata; and/or processing of data for error checking, formatting,compression, decompression, configuring packets, or the like. In certainembodiments, and without limitation to any other aspect of the presentdisclosure, a compute task includes any one or more of: cryptocurrencymining operations, distributed ledger calculations, transactionexecution operations, internal/external data collection operations,and/or digital transformation of data elements, models, or the like. Incertain embodiments, compute resources include any device configured tosupport compute tasks at least during certain operating conditions of asystem, including, without limitation: processor(s), co-processor(s),memory caches, random access memory (RAM), buses. In certainembodiments, compute resources may be provided by a single device and/ordistributed across multiple devices. In certain embodiments, computeresources for a system may be an aggregate of devices, potentiallydistributed and in communication within a single hardware system,through a network (e.g., a LAN, WAN, Wi-Fi, or other communicativecoupling system), through an intranet, and/or through the internet.

Certain features of the present disclosure are referenced as a networktask herein. The term network task should be understood broadly. Incertain embodiments, and without limitation to any other aspect of thepresent disclosure, a network task includes any one or more of:communicating an element of data to a network device (e.g., a packet,data for a packet, and/or metadata or other information about the data);configuring data for a network communication (e.g., compiling into oneor more packets; structuring, dividing, compressing, or combining thedata for network communication); caching, buffering, or otherwisestoring data related to network operations; transmitting data from onedevice to another device (e.g., using a wired or wirelesstransmitting/receiving device and a communication protocol); and/orperforming operations to register or unregister a device from a group ofdevices (e.g., in a mesh network, peer-to-peer network, or other networkconfiguration). In certain embodiments, and without limitation to anyother aspect of the present disclosure, a network task includes any oneor more of: cognitive coordination of network assets; peer bandwidthsharing; transaction execution; spot market testing; internal/externaldata collection; advanced analytics (e.g., of data access, stored data,user or accessor interactions, etc.); smart contract operations;connected insurance operations; and/or distributed ledger storage. Incertain embodiments, any operations performed by a network device,and/or performed to support network communications by a network device,are contemplated as network tasks herein. In certain embodiments,network resources include any device configured to support network tasksat least during certain operating conditions of a system, including,without limitation: networking adapters; networking processors orsub-processors; memory caches or buffers; communication links (e.g.,ports, connectors, wires, transmitters, and/or receivers); networkinfrastructure such as routers, repeaters, hardware comprising a LAN,WAN, intranet, and/or internet; and/or aggregated or abstracted aspectsof a network such as bandwidth or availability of any communicationsystem or communication channel.

It can be seen that, in certain embodiments, a task may be considered acompute task for one system or purpose, and/or a network task foranother system or purpose. Further, a given device may be considered acompute resource for one system or purpose, and/or a network resourcefor another system or purpose. In certain embodiments, a given device ona system may be considered a compute resource under certain operatingconditions and/or for certain considerations, and the given device onthe system may be considered a compute resource under other operatingconditions and/or for certain other considerations. For example, a givenprocessor may be configured to perform operations to execute computerreadable instructions, and therefore be available as a computingresource for determinations made by platform 100 in a first context, andthe same processor may be configured to support network communications(e.g., packaging data, performing network coding, or other networksupport operations), and therefore also be available as a networkresource for the platform 100 in a second context. In another example, aplatform 100 may be performing operations to improve and/or optimizecomputing and/or network resource consumption for a system havingmultiple processors in communication over a network. In the example, theplatform 100 may consider the various processors separately from thenetwork resources—for example distributing the computing tasks acrossprocessors, and calculating the incurred network resource consumptionseparately. Additionally or alternatively, in the example, the platform100 may abstract the network resource consumption associated withdistributing computing tasks across processors as processor resourceconsumption, thereby assigning the associated networking resources thatsupport distribution of processing as processing resources.

One of skill in the art, having the benefit of the present disclosureand information ordinarily available when contemplating a particularsystem, can readily determine which tasks are compute tasks, which tasksare network tasks, which resources are compute resources, and whichresources are network resources for the particular system and at whichoperating conditions of the system. In certain embodiments, for examplewhere improvement or optimization operations are considering bothcompute and network resource optimizations, a particular system mayallow the operations of the platform 100 to determine, or to adjust,which tasks are compute and network tasks, and/or which resources arecompute and network resources. Certain considerations for determiningwhich tasks/resources are compute/network tasks/resources include,without limitation: the limiting aspects of the particular system,including the limiting aspect of the system with time and/or operatingcondition; the system parameters to be improved or optimized; thedesired authority to be given to AI, machine learning, expert system, orother adaptive devices in the system; the cost drivers in the system forvarious devices or features (e.g., infrastructure; support; additionalcommunication paths; upgrades to operating systems, protocols, orfirmware, etc.); the priorities for system improvement between capitalinvestment, operating costs, energy consumption, etc.; and/or thecapacity limitations in the system, including present and futurecapacity, and/or capacities based on time and/or operating condition.

Certain features of the present disclosure are referenced as a datastorage task herein. The term data storage task should be understoodbroadly. In certain embodiments, without limitation to any other aspectof the present disclosure, a data storage task is a task associated withthe storage of data for access at a later time, and/or to support theability to access the data at a later time. Data storage tasks caninclude, without limitation: operations to communicate data to a storagedevice; operations to retrieve stored data from a storage device;operations to store data on the storage device; operations to config.the data for storage or retrieval (e.g., setting or verifyingauthorizations, performing compression or decompression, formatting thedata, and/or summarizing or simplifying the data); operations to movedata from one storage to another (e.g., moving data between short-term,intermediate term, and long-term storage; and/or transferring data fromone data storage location to another to support improvements oroptimizations, such as moving less accessed data to a lower cost storagelocation, etc.); and/or operations to delete stored data. Example andnon-limiting data storage resources include: data storage devices of anytype and storage medium; and/or communication devices and/or processingto support data storage devices. It can be seen that, in certainembodiments, a task may be considered a compute task for one system orpurpose, a network task for another system or purpose, and/or a datastorage task for another system or purpose. Further, a given device maybe considered a compute resource for one system or purpose, a networkresource for another system or purpose, and/or a data storage resourcefor another system or purpose.

Certain features of the present disclosure are referenced as a core taskherein. The term core task should be understood broadly. In certainembodiments, without limitation to any other aspect of the presentdisclosure, a core task is a task associated with a system or facilitythat relates to the function or purpose of that system or facility. Afew examples include, without limitation: a core task for amanufacturing facility relates to the manufacturing operations of thefacility; a core task for a chemical production plant relates to thechemical production operations of the facility; a core task for anautonomous vehicle relates to the operations of the vehicle; and/or acore task for an insurance provider relates to the provision and serviceof insurance products of the provider. In certain embodiments, a coretask includes any related tasks for the facility, which may or may notbe critical or primary tasks for the facility. For example, amanufacturing facility may operate a system to track recyclingoperations, manage parking, and/or tracking the schedules for anintra-company softball league for the manufacturing facility. In certainembodiments, a core task is any task performed for the merits of theunderlying facility, where some increment of data associated to the taskis available, or becomes available, to a platform 100 for considerationin supporting one or more aspects of the task. In certain embodiments, atask may be a core task for certain systems and/or operating conditions,and another type of task (e.g., a compute task, a network task, and/or adata storage task) for other systems and/or other operating conditions.For example, communication of employee e-mails may be a core task forsupporting a manufacturing facility, and may additionally oralternatively be a network task, compute task, and/or data storage task.In a further example, communication of employee e-mails may be a coretask during certain operating periods (e.g., during working hours, foreach employee during that employee's shift period, etc.), and may be anetwork task, compute task, and/or data storage task during otheroperating periods (e.g., during off-hours archiving periods).

Certain features of the present disclosure are referenced as forwardmarkets herein. The term forward market should be understood broadly,and includes any market that provides for trading of any type ofresource scheduled for future delivery of the resource. A forward marketcontemplates formal markets, such as energy trading, commodity trading,compute resource trading, data storage trading, network bandwidthtrading, and/or spectrum trading markets whereby parties can access themarkets and purchase or sell resources (e.g., in a set quantity for aset delivery time). Additionally or alternatively, a forward marketcontemplates an informal market, where parties set forth a mechanism totrade or commit resources that are to be delivered at a later time.Trading may be performed in any currency, or based on in-kindcontributions, and a forward market may be a mechanism for actualdelivery of resources as scheduled, or a mechanism for trading on thefuture value of resources without actual delivery being contemplated(e.g., with some other mechanism that tends to bring the future price into the spot price as the time for each forward looking periodapproaches). In certain embodiments, a forward market may be privatelyoperated, and/or operated as a service where a platform 100 sets up themarket, or communicates with the market. In certain embodiments, asdescribed throughout the present disclosure, transactions on the forwardmarket may be captured in a distributed ledger.

Certain features of the present disclosure are referenced as spotmarkets herein. The term spot market should be understood broadly, andincludes any market that provides for trading of any type of resource ata price based on the current trading price of the resource for immediatedelivery. A spot market contemplates formal markets and/or informalmarkets. Trading on a spot market may be performed in any currency, orbased on in-kind contributions. In certain embodiments, a spot marketmay be privately operated, and/or operated as a service where a platform100 sets up the market, or communicates with the market. In certainembodiments, as described throughout the present disclosure,transactions on the spot market may be captured in a distributed ledger.

Certain features of the present disclosure are referenced as purchasingor sale of one or more resources, including at least: energy, energycredits, network bandwidth (e.g., communication capacity), spectrumand/or spectrum allocation (e.g., certain frequency bandwidths,including potentially transmission rates, transmission power, and/orgeographical limitations); compute resources (or capacity); networkresources (or capacity); data storage resources (or capacity); and/orenergy storage resources (or capacity). A purchase or sale, as utilizedherein, includes any transaction wherein an amount of a resource orother commitment (e.g., an element of intellectual property (IP), an IPlicense, a service, etc.) is traded for a unit of currency of any typeand/or an amount of another resource or commitment. In certainembodiments, a purchase or sale may be of the same type of resource orcommitment, for example where energy for one time period (e.g.,immediate delivery, or a first future time period) is traded for energyat another time period (e.g., a second future time period, which isdistinct from the immediate delivery or the first future time period).In certain embodiments, one side of the purchase or sale includes acurrency of any type, including at least a sovereign currency, acryptocurrency, and/or an arbitrary agreed upon currency (e.g., specificto a private market or the like).

Certain features of the present disclosure are referenced as a machineherein. The term machine, as utilized herein, should be understoodbroadly. In certain embodiments, a machine includes any componentrelated to a facility having at least one associated task, which may bea core task, a compute task, a network task, a data storage task, and/oran energy storage task. In certain embodiments, a machine includes anycomponent related to a facility that utilizes at least one resource,which may be an energy resource, a compute resource, a network resource,and/or a data storage resource. In certain embodiments, a machineincludes any one or more aspects of any controller, AI implementingdevice, machine learning implementing device, deep learning implementingdevice, neural network implementing device, distributed ledgerimplementing or accessing device, intelligent agent, a circuitconfigured to perform any operations described throughout the presentdisclosure, and/or a market (forward and/or spot) implementing oraccessing device as described throughout the present disclosure. Incertain embodiments, a machine is operatively and/or communicativelycoupled to one or more facility components, market(s), distributedledger(s), external data, internal data, resources (of any type), and/orone or more other machines within a system. In certain embodiments, twoor more machines be provided with at least one aspect of cooperationbetween the machines, forming a fleet of machines. In certainembodiments, two machines may cooperate for certain aspects of a systemor in certain operating conditions of the system, and thereby form afleet of machines for those aspects or operating conditions, but may beseparate individually operating machines for other aspects or operatingconditions. In certain embodiments, machines forming a part of a fleetof machines may be associated with (e.g., positioned at, communicativelycoupled to, and/or operatively coupled to) the same facility, ordistinct facilities. In certain embodiments, a machine may be associatedwith more than one facility, and/or associated with different facilitiesat different times or operating conditions.

Certain aspects of the present disclosure are referenced as energycredits herein. The term energy credits, as utilized herein, should beunderstood broadly. In certain embodiments, an energy credit is aregulatory, industry agreed, or other indicia of energy utilization thatis tracked for a particular purpose, such as CO₂ emissions, greenhousegas emissions, and/or any other emissions measure. In certainembodiments, an energy credit may be “negative” (e.g., relating toincreased emissions) or “positive” (e.g., relating to reducedemissions). In certain embodiments, energy credits may relate toparticular components (e.g., automobiles of a certain power rating orapplication, computing related energy utilization, etc.) and/or genericenergy utilization (e.g., without regard to the specific applicationutilizing the energy). In certain embodiments, energy credits may relateto taxation schemes, emissions control schemes, industry agreementschemes, and/or certification schemes (e.g., voluntary, involuntary,standards-related, or the like). In certain embodiments, an energycredit includes any indicia of energy utilization where verifiedtracking (e.g., for reporting purposes) of that indicia can be utilizedto increment or decrement value for a facility, facility owner, orfacility operator. Non-limiting examples include: an entity subject to aregulatory requirement for reporting emissions; and/or an entityreporting emissions performance in a public format (e.g., an annualreport).

Certain aspects of the present disclosure are referenced as collectiveoptimization. Collective optimization, as used herein, includes theimprovement and/or optimization of multiple aspects of a system (e.g.,multiple machines, multiple components of a facility, multiplefacilities, etc.) together as an optimized or improved system. It willbe understood that collective optimization may occur within more thanone dimension—for example a collectively optimized or improved systemmay have a higher overall energy consumption than before operations tocollectively optimize or improve, but have improvement in some otheraspect (e.g., utilization of energy credits, lower cost of operation,superior product or outcome delivery, lower network utilization, lowercompute resource usage, lower data storage usage, etc.).

Certain aspects of the present disclosure are referenced as social mediadata sources. Social media data sources include, without limitation:information publicly available on any social media site or other massmedia platform (e.g., from comments sections of news articles; reviewsections of an online retailer; publicly available aspects of profiles,comments, and/or reactions of entities on social media sites; etc.);proprietary information properly obtained from any social media site orother mass media platform (e.g., purchased information, informationavailable through an accepted terms of use, etc.); and the like. Incertain embodiments, social media data sources include cross-referencedand/or otherwise aligned data from multiple sources—for example where acomment from one site is matched with a profile from another site, datais matched with a member list from a professional group membership, datais matched from a company website, etc. In certain embodiments, socialmedia data sources include cross-referenced and/or otherwise aligneddata from other data sources, such as IoT data sources, automated agentbehavioral data sources, business entity data sources, human behavioraldata sources, and/or any other data source accessible to a machine,platform 100, or other device described throughout the presentdisclosure.

Certain aspects of the present disclosure reference determining (and/oroptimizing, improving, reducing, etc.) the utilization or consumption ofenergy or resources. Determining the utilization or consumption ofenergy or resources should be understood broadly, and may includeconsideration of a time value of the consumption, and/or anevent-related value of the consumption (e.g., calendar events such asholidays or weekends, and/or specific real-time events such as newsrelated events, industry related events, events related to specificgeographical areas, and the like). In certain embodiments, theutilization or consumption of energy or resources may includeconsideration of the type of energy or resource (e.g., coal-generatedelectricity versus wind-generated electricity), the source of the energyor resource (e.g., the geographical origin of the energy available, theentity providing a compute resource, etc.), the total capacity of theenergy or resource (e.g., within a facility or group of facilities, froma third-party, etc.), and/or non-linear considerations of the cost ofthe energy or resource (e.g., exceeding certain thresholds, the likelycost behavior in a market responsive to a purchase event, etc.).

Certain aspects of the present disclosure reference performingoperations to implement an arbitrage strategy. An arbitrage strategy asutilized herein should be understood broadly. An arbitrage strategyincludes any strategy structured to favorably utilize a differentialbetween a present value of a resource and a predicted future value ofthe resource. In certain embodiments, implementing an arbitrage strategyincludes a determination that a given value (either a present value on aspot market, or a future value for at least one time frame) of theresource is abnormally low or high relative to an expected oranticipated value, and to execute operations to either purchase or sellthe resource and benefit from the abnormal value. In certainembodiments, an arbitrage strategy is implemented as a portion of anoverall optimization or improvement operation. For example, in certainembodiments implementing the arbitrage strategy may push the overallsystem away from the otherwise optimum value (e.g., buying or sellingmore of a resource than the improved or optimized system would otherwiseperform), and the benefits of the implementation of the arbitragestrategy are considered within the value of the entire system. Incertain embodiments, an arbitrage strategy is implemented as astandalone transaction (e.g., for a system that is not presentlyoperating any core tasks, and/or a system where implementing anarbitrage strategy is the core task), and the arbitrage strategy isimplemented as the primary, or the only, system level improvement oroptimization.

Certain aspects of the present disclosure are referenced as a smalltransaction, and/or a rapidly executed transaction. A small transactionas utilized herein references a transaction that is small enough tolimit the risk of the transaction to a threshold level, where thethreshold level is either a level that is below an accepted cost of thetransaction, or below a disturbance level (e.g., a financialdisturbance, an operational disturbance, etc.) for the system. Forexample, wherein an implementation of an arbitrage strategy includes asmall transaction for an energy resource, the small transaction may beselected to be small enough such that the amount of energy bought orsold does not change the basic operational equilibrium of the systemunder current operating conditions, and/or such that the amount ofpotential loss from the transaction is below a threshold value (e.g., anarbitrage fund, an operating cash amount, or the like). In certainembodiments, the small transaction is selected to be large enough totest the arbitrage opportunity determination—for example a large enoughtransaction that the execution of the transaction will occur in asimilar manner (e.g., not likely to be absorbed by a broker, having anexpected similarity in execution speed, and/or having an expectedsimilarity in successful execution likelihood) to a planned larger tradeto be performed. It will be understood that more than one smalltransaction, potentially of increasing size, may be performed before alarger transaction is performed, and/or that a larger transaction may bedivided into one or more portions. A rapidly executed transactionincludes any transaction that is expected to have a rapid time constantwith regard to the expected time frame of the arbitrage opportunity. Forexample, where a price anomaly is expected to persist for one hour, arapidly executed transaction may be a transaction expected to clear inmuch less than one hour (e.g., less than half of the hour, to providetime to execute the larger transaction). In another example, where aprice anomaly is expected to persist for 10 minutes, a rapidly executedtransaction may be a transaction expected to clear in much less than 10minutes. It will be understood that any machine, AI component, machinelearning component, deep learning component, expert system, controller,and/or any other adaptive component described throughout the presentdisclosure may adaptively improve the size, timing, and/or number ofsmall transactions and large transactions as a part of improving oroptimizing an implementation of an arbitrage strategy. Additionally oralternatively, any parameters of the arbitrage determination, such asthe expected value of the arbitrage opportunity and/or the expectedpersistence time of the arbitrage opportunity, may be adaptivelyimproved.

Certain aspects of the present disclosure are referenced as a token,and/or certain operations of the present disclosure are referenced astokenizing one or more aspects of data or other parameters. Tokens,and/or operations to tokenize, should be understood broadly, and includeoperations and/or data utilized to abstract underlying data and/or toprovide confirmable or provable access to the underlying data. Withoutlimitation to any other aspect of the present disclosure, tokens includewrapper data that corresponds to underlying data values, hashes orhashing operations, surrogate values, and/or compartmentalized data.Tokenization operations may include hashing, wrapping, or other dataseparation and/or compartmentalization operations, and may furtherinclude authorization operations such as verification of a user or otherinteracting party, including verification checks based on IP addresses,login interfaces, and/or verifications based on characteristics of theuser or other interacting party that are accessible to the tokenizingsystem. In certain embodiments, a token may include certain aspects ofthe underlying or tokenized data (e.g., headers, titles, publiclyavailable information, and/or metadata), and/or a token may be entirelyabstracted from the underlying or tokenized data. In certainembodiments, tokens may be utilized to provide access to encrypted orisolated data, and/or to confirm that access to the encrypted orisolated data has been provided, or that the data has been accessed.

Certain aspects of the present disclosure reference provable access(e.g., to data, instruction sets, and/or IP assets). Provable access, asutilized herein, should be understood broadly. In certain embodiments,provable access includes a recordation of actual access to the data, forexample recording a data value demonstrating the data was accessed, andmay further include user or accessor information such as usernames,e-mail addresses, IP addresses, geographic locations, time stamps,and/or which portions of the data were accessed. In certain embodiments,provable access includes a recordation of the availability of the datato a user or potential accessor, and may further include user oraccessor information such as usernames, e-mail addresses, IP addresses,geographic locations, time frames or stamps, and/or which portions ofthe data were available for access. In certain embodiments, provableaccess includes storing the recordation on a system (e.g., on adistributed ledger, and/or in a memory location available to anycontroller, machine, or other intelligent operating entity as describedthroughout the present disclosure). In certain embodiments, provableaccess includes providing the user or accessor of the data with a datavalue such that the user or accessor is able to demonstrate the accessor access availability. In certain embodiments, a data value and/ordistributed ledger entry forming a portion of the provable access may beencrypted, tokenized, or otherwise stored in a manner whereby theprovable access can be verified, but may require an encryption key,login information, or other operation to determine the access or accessavailability from the data value or distributed ledger entry.

Certain aspects of the present disclosure are referenced as aninstruction set and/or as executable algorithmic logic. An instructionset and/or executable algorithmic logic as referenced herein should beunderstood broadly. In certain embodiments, an instruction set orexecutable algorithmic logic includes descriptions of certain operations(e.g., flow charts, recipes, pseudo-code, and/or formulas) to performthe underlying operations—for example an instruction set for a processmay include a description of the process that may be performed toimplement the process. In certain embodiments, an instruction set orexecutable algorithmic logic includes portions of certain operations,for example proprietary, trade secret, calibration values, and/orcritical aspects of a process, where the remainder of the process may begenerally known, publicly available, or provided separately from theportions of the process provided as an instruction set or executablealgorithmic logic. In certain embodiments, an instruction set orexecutable algorithmic logic may be provided as a black box, whereby theuser or accessor of the instruction set or executable algorithmic logicmay not have access to the actual steps or descriptions, but mayotherwise have enough information to implement the instruction set orexecutable algorithmic logic. For example, and without limitation, ablack box instruction set or executable algorithmic logic may have adescription of the inputs and outputs of the process, enabling the useror accessor to include the instruction set or executable algorithmiclogic into a process (e.g., as a module of executable instructionsstored in a computer readable medium, and/or as an input to a machineresponsive to the black box operations) without having access to theactual operations performed in the instruction set or the executablealgorithmic logic.

Certain aspects of the present disclosure are referenced as adistributed ledger. A distributed ledger, as referenced herein, shouldbe understood broadly. Without limiting any other aspect of the presentdisclosure, a distributed ledger includes any data values that areprovided in a manner to be stored in distributed locations (e.g., storedin multiple memory locations across a number of systems or devices),such that individual members of the distributed system can add datavalues to the set of data values, and where the distributed system isconfigured to verify that the added data values are consistent with theentire set of data values, and then to update the entire set of datavalues thereby updating the distributed ledger. A block chain is anexample implementation of a distributed ledger, where a critical mass(e.g., more than half) of the distributed memory locations createagreement on the data values in the distributed ledger, thereby creatingan updated version of the distributed ledger. In certain embodiments, adistributed ledger may include a recordation of transactions, storeddata, stored licensing terms, stored contract terms and/or obligations,stored access rights and/or access events to data on the ledger, and/orstored instruction sets, access rights, and/or access events to theinstruction sets. In certain embodiments, aspects of the data on adistributed ledger may be stored in a separate location, for examplewith the distributed ledger including a pointer or other identifyinglocation to the underlying data (e.g., an instruction set storedseparately from the distributed ledger). In certain embodiments, anupdate to the separately stored data on the distributed ledger mayinclude an update to the separately stored data, and an update to thepointer or other identifying location on the distributed ledger, therebyupdating the separately stored data as referenced by the distributedledger. In certain embodiments, a wrapper or other interface object withthe distributed ledger may facilitate updates to data in the distributedledger or referenced by the distributed ledger, for example where aparty submits an updated instruction set, and where the wrapper storesthe updated instruction set separately from the distributed ledger, andupdates the pointer or identifying location on the distributed ledger toaccess the updated instruction set, thereby creating a modifiedinstruction set (or other data).

Certain aspects of the present disclosure are referenced as a wrapper,expert wrapper, a smart wrapper, and/or a smart contract wrapper. Awrapper, as referenced herein, should be understood broadly. Withoutlimitation to any other aspect of the present disclosure, a wrapperreferences any interfacing system, circuit, and/or computer executableinstructions providing an interface between the wrapped object (e.g.,data values and/or a distributed ledger) and any system, circuit,machine, user, and/or accessor of the wrapped object. A wrapper, incertain embodiments, provides additional functionality for the wrappedobject, user interfaces, API, and/or any other capabilities to implementoperations described herein. In certain embodiments, a wrapper canprovide for access authorization, access confirmation, data formatting,execution of agreement terms, updating of agreement terms, data storage,data updating, creation and/or control of metadata, and/or any otheroperations as described throughout the present disclosure. In certainembodiments, parameters of the wrapper (e.g., authorized users, data ina stack of data, creation of new data stacks, adjustments to contractterms, policies, limitations to total numbers of users or data values,etc.) may be configurable by a super user, an authorized user, an owner,and/or an administrator of the wrapper, and/or parameters of the wrappermay be accessible within the wrapped object (e.g., as data values storedin a distributed ledger which, when updated, change certain parametersof the wrapper). An expert wrapper or a smart wrapper includes, withoutlimitation, any wrapper that includes or interacts with an expertsystem, an AI system, a ML system, and/or an adaptive system.

Certain aspects of the present disclosure are referenced as IP licensingterms and/or contract terms herein. IP licensing terms, as used herein,should be understood broadly. Without limitation to any other aspect ofthe present disclosure, IP licensing terms include permissions to accessand/or utilize any element of IP. For example, and IP licensing term mayinclude an identification of the IP element (e.g., a trade secret; apatent and/or claims of a patent; an image, media element, writtendescription, or other copyrighted data element; and/or proprietaryinformation), a description of the access or usage terms (e.g., how theIP element may be utilized by the accessor), a description of the scopeof the utilization (e.g., time frames, fields of use, volume or otherquantitative limits, etc.), a description of rights relating to the IPelement (e.g., derivative works, improvements, etc.), and/or adescription of sub-licensing rights (e.g., provisions for suppliers,customers, affiliates, or other third parties that may interact with theuser or accessor in a manner related to the IP element). In certainembodiments, IP licensing terms may include a description of theexclusivity or non-exclusivity provided in relation to an IP element. Incertain embodiments, IP licensing terms may relate to open source terms,educational use, non-commercial use, or other IP utilization terms thatmay or may not relate to a commercial transaction or an exchange ofmonetary/currency value, but may nevertheless provide for limitations tothe use of the IP element for the user or accessor. Without limitationto any other aspect of the present disclosure, contract terms includeany one or more of: options (e.g., short and/or put options relating toany transaction, security, or other tradeable assets); fieldexclusivity; royalty stacking (e.g., distribution of royalties between agroup of owners and/or beneficiaries); partial exclusivity (e.g., byfields-of-use, geographic regions, and/or transaction types); pools(e.g., shared or aggregated IP stacks, data pools, and/or resourcepools); standard terms; technology transfer terms; performance-relatedrights or metrics; updates to any of the foregoing; and/or userselections (e.g., which may include further obligations to the userand/or costs to the user) of any of the foregoing.

Certain aspects herein are described as a task system. A task systemincludes any component, device, and/or group of components or devicesthat performs a task (or a portion of a task) of any kind describedthroughout the present disclosure, including without limitation any typeof task described for a machine. The task may have one or moreassociated resource requirements, such as energy consumption, energystorage, data storage, compute requirements, networking requirements,and/or consumption of associated credits or currency (e.g., energycredits, emissions credits, etc.). In certain embodiments, the resourceutilization of the task may be negative (e.g. consumption of theresource) or positive (e.g., regeneration of energy, deletion of data,etc.), and may further include intermediate values or time trajectoriesof the resource utilization (e.g., data storage requirements that varyover an operating period for the task, energy storage requirements thatmay fill or deplete over an operating period, etc.). The determinationof any resource requirement for a task herein should be understoodbroadly, and may be determined according to published information fromthe task system (e.g., according to a current load, energy consumption,etc.), determined according to a scheduled or defined value (e.g.,entered by an operator, administrator, and/or provided as acommunication by a controller associated with the task system), and/ormay be determined over time, such as by observing operating histories ofthe task system. In certain embodiments, expert systems and/or machinelearning components may be applied to determine resource requirementsfor a task system—for example determining relationships between anyavailable data inputs and the resource requirements for upcoming tasks,which allows for continuous improvement of resource requirementdeterminations, and further allows for training of the system todetermine which data sources are likely to be predictive of resourcerequirements (e.g., calendar date, periodic cycles, customer orders orother business indicators, related industry indicators, social mediaevents and/or other current events that tend to drive resourcerequirements for a particular task, etc.).

Referencing FIG. 40, an example system 4000 is a transaction-enablingsystem including a smart contract wrapper 4002. The contract wrapper4002 may be configured to access a distributed ledger 4004 including anumber of embedded contract and/or IP licensing terms 4006 and a numberof data values 4008, and to interpret an access request value 4010 forthe data values 4008. In response to the access request value 4010, thecontract wrapper provides access to at least a portion of the datavalues 4008, and commits an entity 4009 (e.g., a subscribing user, acustomer, and/or a prospective customer for the data) providing theaccess request value 4010 to at least one of the embedded contractand/or IP licensing terms 4006. The contract wrapper 4002 may beembodied as computer readable instructions that provide a user interfacebetween a user and the distributed ledger, and may further beimplemented as a web interface, an API, a computer program product, orthe like. The contract wrapper 4002 is operationally positioned betweenone or more users 4009 and the distributed ledger 4004 and data values4008, but the contract wrapper 4002 may be stored and function in aseparate physical location from the distributed ledger 4004, data values4008, and/or the users 4009. Additionally or alternatively, the contractwrapper 4002 may be distributed across multiple devices and/orlocations.

An example embodiment includes the data values 4008 includingintellectual property (IP) data corresponding to a plurality of IPassets 4016, such as a listing, description, and/or summary informationfor patents, trade secrets, or other proprietary information, and theembedded contract and/or IP licensing terms 4006 include a number ofintellectual property (IP) licensing terms (e.g., usage rights, fieldsof use, limitations, time frames, royalty rates, and the like) for thecorresponding IP assets. In certain embodiments, the data values 4008may include the IP assets 4016 (e.g., proprietary information, recipes,instructions, or the like), and/or the data values 4008 may correlate toIP assets 4016 stored elsewhere, and may further include sufficientinformation for a user to understand what is represented in the IPassets 4016. The example contract wrapper 4002 may further commit theentity 4009 providing the access request value 4010 to correspondingcontract and/or IP licensing terms 4006 for accessed ones of the IPassets 4016—for example only committing the user to terms for assets4016 that are agreed upon, accessed, and/or utilized (e.g. committedcontract terms).

An example contract wrapper 4002 is further configured to interpret anIP description value 4012 and an IP addition request 4014, and to addadditional IP data to the data values 4008 in response to the IPdescription value 4012 and the IP addition request 4014, where theadditional IP data includes IP data corresponding to an additional IPasset. For example, the contract wrapper 4002 may accept an IPdescription value 4012 from a user (e.g., a document, reference number,or the like), and respond to the IP addition request 4014 to addinformation to the data values 4008 consistent with the IP descriptionvalue 4012, thereby adding one or more IP assets 4016 to the data values4008. In certain embodiments, the contract wrapper 4002 may furtherprovide a user interface to interact with the user 4009 or other entityadding the IP asset, which may include determining permissions to add anasset, and/or consent or approval from the user or other parties. Incertain embodiments, consent or approval may be performed through rules,an intelligent system, or the like, for example to ensure that IP assetsbeing added are of a selected type, quality, valuation, or other haveother selected characteristics.

An example contract wrapper 4002 accesses a number of owning entitiescorresponding to the IP assets of the data values 4008. The examplecontract wrapper 4002 apportions royalties 4018 generated from the IPassets corresponding to the data values 4008 in response to thecorresponding IP license terms 4006, such as apportionment based onasset valuations, asset counts, and/or any agreed upon apportionmentparameters. In certain embodiments, the contract wrapper 4002 adds an IPasset to an aggregate stack of IP assets based on an IP addition request4014, and updates the apportionment of royalties 4018 based upon theowning entities 4009 and IP assets 4016 for the aggregate stack afterthe addition of the IP asset. In certain embodiments, the contractwrapper 4002 is configured to commit the entity adding the IP asset tothe IP licensing terms 4006, and/or the IP licensing terms 4006 asamended with the addition of the new IP assets.

An example transaction-enabling system 4000 including a smart contractwrapper 4002, the smart contract wrapper according to one disclosednon-limiting embodiment of the present disclosure may be configured toaccess a distributed ledger includes a plurality of intellectualproperty (IP) licensing terms 4006 corresponding to a plurality of IPassets 4016, wherein the plurality of IP assets 4016 include anaggregate stack of IP, and to interpret an IP description value 4012 andan IP addition request 4014, and, in response to the IP addition request4014 and the IP description value 4012, to add an IP asset to theaggregate stack of IP. An example smart contract wrapper 4002 interpretsan IP licensing value 4020 corresponding to the IP description value4012, and to add the IP licensing value 4020 to the plurality of IPlicensing terms 4006 in response to the IP description value and the IPaddition request. The IP licensing value 4020 may be determined frominput by the user 4009, automated or machine learning improvedoperations performed on indicia of the added IP asset (e.g., accordingto valuation algorithms such as markets affected by the IP asset, valuecontributions of the IP asset, participation of the IP asset intoindustry standard systems or operations, references to the IP asset byother IP assets, and the like), and/or may further depend upon the roleor permissions of the user 4009. In certain embodiments, a first user4009 adds the IP asset to the IP assets 4016, and a second user 4009provides additional data utilized to determine the IP licensing value4020. An example smart contract wrapper 4002 further associates one ormore contract and/or IP licensing terms 4006 to the added IP asset. Incertain embodiments, one or more IP assets are stored within the datavalues 4008, and/or are referenced to a separate data store having theIP assets 4016. An example aggregate stack of IP further includes areference to the data store for one or more IP assets 4016.

Referencing FIG. 41, an example procedure 4100 to execute a smartcontract wrapper is depicted. The procedure 4100 includes an operation4102 to access a distributed ledger including a number of embeddedcontract terms and a number of data values, an operation 4104 to providea user interface (UI) including an access option, an operation 4106 toprovide a user interface including acceptance input portion, and anoperation 4108 to interpret an access request value for the data values.The example procedure 4100 further includes an operation 4110 to provideaccess to at least a portion of the plurality of data values, and anoperation 4112 to commit an entity providing the access request value toat least one of the plurality of embedded contract terms.

An example procedure may include providing the entity providing theaccess request value with a user interface including a contractacceptance input, and where the providing access and committing theentity is in response to a user input on the user interface. An exampleprocedure may include the data values including IP data (e.g., IPelements, or information corresponding to IP elements), where theembedded contract terms include IP licensing terms for the correspondingIP assets. An example procedure further includes the operation 4112 tocommit an entity providing the access request value to corresponding IPlicensing terms for accessed IP assets.

Referencing FIG. 42, an example procedure 4200 includes an operation4202 to interpret an IP description value and an IP addition request,and an operation 4204 to add additional IP data to a plurality of datavalues on a distributed ledger in response to the IP description valueand the IP addition request, wherein the additional IP data includes IPdata corresponding to an additional IP asset. An example procedure 4200further includes an operation 4206 to further interpret an IP additionentity (e.g., the entity adding the IP data, and/or an entity designatedas an owner of the additional IP asset by the entity adding the IPdata), and an operation 4208 to apportion royalties from the pluralityof IP assets to the plurality of owning entities in response to thecorresponding IP licensing terms, and/or further in response to theadditional data and the IP addition entity.

Referencing FIG. 43, an example procedure 4300 includes an operation4302 to access a distributed ledger, an operation 4304 to interpret anIP description and an IP addition request, an operation 4306 to add IPasset(s) to the distributed ledger in response to the IP description andthe IP addition request, an operation 4308 to associate one or morelicensing terms to the added IP, and an operation 4310 to apportionroyalties from IP assets of the distributed ledger to owning entities ofthe IP assets. An example procedure 4300 further includes an operation4312 to interpret an added IP entity (e.g., as a change to an existingIP asset, and/or an entity owning an added IP asset), and an operation4314 to commit the added IP entity to one or more of the IP licensingterms.

Referencing FIG. 44, an example procedure 4400 includes an operation4402 to evaluate IP assets for a distributed ledger, an operation 4404to determine that one or more assets have expired (e.g., according tothe terms of the licensing agreements, according to a date defined withthe IP asset, and/or according to external data such as a databaseupdated in response to a court ruling or other decision about theasset). The example procedure 4400 further includes an operation 4406 todetermine or update a valuation of the IP assets, an operation 4408 todetermine whether an ownership change has occurred for one or more ofthe IP assets, an operation 4410 to commit updated entities to the IPlicensing terms, and/or an operation 4412 to update apportionment ofroyalties to owning entities in response to an asset expiration, assetvaluation change, and/or a change in an owning entity for one or more IPassets. In certain embodiments, an example procedure 4400 includes anoperation to provide a user interface to a new owning entity of at leastone of the IP assets, and an operation to commit a new owning entity toone or more of the IP licensing terms in response to a user input on theuser interface.

In certain embodiments, IP assets described herein include a listing ofIP assets, an aggregated stack of IP assets, and/or any otherorganization of IP assets. In certain embodiments, IP assets may begrouped, sub-grouped, clustered, or organized in any other manner, andlicensing terms may be associated, in whole or part, with the groups,sub-groups, and/or clusters of IP assets. In certain embodiments, anumber of IP assets may be within a first aggregate stack for a firstpurpose (e.g., a particular field of use, type of accessing entity,etc.), but within separate aggregated stacks for a second purpose (e.g.,a different field of use, type of accessing entity, etc.).

Referencing FIG. 45, an example transaction-enabling system 4500includes a controller 4502, the controller 4502 according to onedisclosed non-limiting embodiment of the present disclosure may beconfigured to: access a distributed ledger 4004 including an instructionset 4504, tokenize the instruction set, interpret an instruction setaccess request 4506, and, in response to the instruction set accessrequest 4506, provide a provable access 4508 to the instruction set. Incertain embodiments, the controller 4502 provides provable access 4508by recording a value on the distributed ledger 4004, providing a user4009 with a token demonstrating access, and/or records a transaction4510 on the distributed ledger 4004 indicating the access. In certainembodiments, the instruction set 4504 may include an instruction set forany one or more of the following: a coating process, a 3D printer (e.g.,a build file, diagram, and/or operational instructions), a semiconductorfabrication process, a field programmable gate array (FPGA) instructionset, a food preparation instruction set (e.g., an industrial process, aformula, a recipe, etc.), a polymer production instruction set, achemical synthesis instruction set, a biological production processinstruction set, and/or a crystal fabrication system.

An example controller 4502 further interprets an execution operation4512 of the instruction set, and records a transaction 4510 on thedistributed ledger 4004 in response to the execution operation 4512. Incertain embodiments, interpreting an execution operation 4512 includesdetermining that a user has accessed the instruction set 4504sufficiently to determine a process described in the instruction set4504, determining that a user, the controller, or another aspect of thesystem 4500 has provided instructions to a device responsive to theinstruction set 4504, and/or receiving a confirmation, data value, orother communication indicating that the instruction set 4504 has beenexecuted. In certain embodiments, one or more instruction sets 4504stored on the distributed ledger 4004 may be at least partially storedin a separate data store of instructions 4516, where the distributedledger 4004 may store references, partial instructions, summaries, orthe like, and access the separate data store of instructions 4516 asneeded. In certain embodiments, one or more instruction sets 4504 may bestored on the distributed ledger 4004. In certain embodiments, access tothe instruction set(s) 4504 may be provided in accordance with one ormore contract terms 4006, and/or may be provided in response tocommitting a user or accessing entity to the one or more contract terms4006.

Referencing FIG. 46, an example procedure 4600 includes an operation4602 to access a distributed ledger including an instruction set, anoperation 4604 to tokenize the instruction set, and an operation 4606 tointerpret an instruction set access request. The example procedure 4600includes, in response to the instruction set access request, anoperation 4608 to provide provable access to the instruction set. Incertain embodiments, the instruction set may include an instruction setfor any one or more of the following: a coating process, a 3D printer(e.g., a build file, diagram, and/or operational instructions), asemiconductor fabrication process, a field programmable gate array(FPGA) instruction set, a food preparation instruction set (e.g., anindustrial process, a formula, a recipe, etc.), a polymer productioninstruction set, a chemical synthesis instruction set, a biologicalproduction process instruction set, and/or a crystal fabrication system.An example procedure 4600 further includes an operation 4610 to commanda downstream tool (e.g., a production tool for a process, an industrialmachine, a controller for an industrial process, a 3D printing machine,etc.) using the instruction set. In certain embodiments, the instructionset includes an FPGA instruction set. In certain embodiments, theprocedure 4600 includes an operation to determine that an executionoperation related to the instruction set has occurred, and/or anoperation 4612 to record a transaction on the distributed ledger inresponse to an access of the instruction set, operation 4610 to commanda downstream tool using the instruction set, and/or an executionoperation related to the instruction set.

Referencing FIG. 47, an example transaction-enabling system 4700includes a controller 4502, the controller 4502 according to onedisclosed non-limiting embodiment of the present disclosure may beconfigured to: access a distributed ledger 4004 including executablealgorithmic logic 4704, tokenize the executable algorithmic logic,interpret an executable algorithmic logic access request 4706, and, inresponse to the executable algorithmic logic access request 4706,provide a provable access 4708 to the executable algorithmic logic. Incertain embodiments, the controller 4502 provides provable access 4708by recording a value on the distributed ledger 4004, providing a user4009 with a token demonstrating access, and/or records a transaction4510 on the distributed ledger 4004 indicating the access. In certainembodiments, the executable algorithmic logic 4704 may include logicaldescriptions, computer readable and executable code in any formincluding source code and/or assembly language, a black box executablecode or function (e.g., having an API, embedded code, and/or a codeblock for a specified program such as Matlab™ Simulink™, LabView™,Java™, or the like). In certain embodiments, the algorithmic logic 4704further includes, and/or is communicated with, an interface description4718 (e.g., providing input and output values, ranges, and/or formats;time values such as sampling rates, time constants, or the like; and/orparameter descriptions including required values, optional values, flagsor enable/disable settings for features, and/or may further include orbe communicated with documentation, instructions, or the like for thealgorithmic logic 4704. In certain embodiments, the interfacedescription 4718 may be provided as an API to the algorithmic logic4704, and/or may be provided to support an API implementation to accessthe algorithmic logic.

An example controller 4502 further interprets an execution operation4712 of the algorithmic logic, and records a transaction 4510 on thedistributed ledger 4004 in response to the execution operation 4712. Incertain embodiments, interpreting an execution operation 4712 includesdetermining that a user has accessed the algorithmic logic 4704sufficiently to determine a process described in the algorithmic logic4704, determining that a user, the controller, or another aspect of thesystem 4700 has provided execution instructions to a device responsiveto the algorithmic logic 4704, and/or receiving a confirmation, datavalue, or other communication indicating that the algorithmic logic 4704has been executed, downloaded, and/or copied. In certain embodiments,one or more algorithmic logic elements stored on the distributed ledger4004 may be at least partially stored in a separate data store 4716,where the distributed ledger 4004 may store references, partialinstructions, documentation, interface descriptions 4718, summaries, orthe like, and access the separate data store 4716 of algorithmic logicelements as needed. In certain embodiments, one or more algorithmiclogic 4704 elements may be stored on the distributed ledger 4004. Incertain embodiments, access to the algorithmic logic 4704 elements maybe provided in accordance with one or more contract terms 4006, and/ormay be provided in response to committing a user or accessing entity tothe one or more contract terms 4006.

Referencing FIG. 48, an example procedure 4800 includes an operation4802 to access a distributed ledger including executable algorithmiclogic, an operation 4804 to tokenize the executable algorithmic logic,and an operation 4806 to interpret an access request for the executablealgorithmic logic. An example procedure 4800 further includes, inresponse to the access request, an operation 4808 to provide provableaccess to the executable algorithmic logic. In certain embodiments, theprocedure 4800 includes an operation 4810 to provide an interface forthe algorithmic logic (e.g., as an API, and/or any other interfacedescription provided throughout the present disclosure). An exampleprocedure 4800 includes an operation to interpret an execution operationof the executable algorithmic logic, and/or an operation 4812 to recorda transaction on the distributed ledger in response to the executionoperation.

Referencing FIG. 49, an example transaction-enabling system 4900includes a controller 4502, where the controller may be configured toaccess a distributed ledger 4004 including a firmware data value 4904,to tokenize the firmware data value, and to interpret an access request4906 for the firmware data value. The example controller 4502 may befurther configured to, in response to the access request 4906, provide aprovable access 4908 to a firmware corresponding to the firmware datavalue 4904. In certain embodiments, the controller 4502 providesprovable access 4908 by recording a value on the distributed ledger4004, providing a user 4009 with a token demonstrating access, and/orrecords a transaction 4510 on the distributed ledger 4004 indicating theaccess. In certain embodiments, the firmware data value 4904 may includethe corresponding firmware (e.g., as stored code, an installationpackage, etc.), and/or may include a reference to the firmware and/orrelated metadata (e.g., dates, versions, size of the firmware,applicable components, etc.). In certain embodiments, one or moreaspects of the firmware or the firmware data value(s) 4904 may be storedin a separate data store 4916 accessible to the controller 4502 and/orthe distributed ledger 4004.

An example controller 4502 is further configured to determine that afirmware update 4918 has occurred for a firmware data value 4904, and toprovide an update notification 4912 to an accessor of the firmware datavalue 4904 in response to the firmware update 4918—for example to ensurethat a current user or accessor receives (or chooses not to receive) theupdated firmware data value 4904, and/or to notify a previous user oraccessor that an update of the firmware data value 4904 has occurred. Anexample controller 4502 is further configured to interpret a firmwareutilization value 4920 (e.g., a download operation, installationoperation, and/or execution operation of the firmware data value 4904),and/or may further record a transaction 4510 on the distributed ledger4004 in response to the firmware utilization value 4920. In certainembodiments, the firmware data value 4904 may include firmware for acomponent of a production process, and/or firmware for a productiontool. Example and non-limiting production tools include tools for aprocess such as: a coating process, a 3D printing process, asemiconductor fabrication process, a food preparation process, a polymerproduction process, a chemical synthesis process, a biologicalproduction process, and/or a crystal fabrication process. In certainembodiments, the firmware data value 4904 may include firmware for acompute resource and/or firmware for a networking resource.

Referencing FIG. 50, an example procedure 5000 includes an operation5002 to access a distributed ledger including a firmware data value, anoperation 5004 to tokenize the firmware data value on the distributedledger, an operation 5006 to interpret an access request for thefirmware data value, and, in response to the access request, anoperation 5008 to provide a provable access to the firmwarecorresponding to the firmware data value. The example procedure 5000further includes an operation 5010 to determine whether an update to thefirmware is available. In response to the operation 5010 determining“YES”, the procedure 5000 includes an operation 5012 to notify anaccessor of the firmware update. In response to the operation 5010determining “NO”, and/or following the notification operation 5012, theexample procedure 5000 includes an operation 5014 to record atransaction on the distributed ledger. The operation 5014 may beresponsive to access, downloading, executing, updating, and/orinstalling of the firmware and/or a firmware asset.

Referencing FIG. 51, an example transaction-enabling system 5100includes a controller 4502, which may be configured to access adistributed ledger 4004 including serverless code logic 5104, totokenize the serverless code logic, and to interpret an access request5106 for the serverless code logic. The example controller 4502 isfurther configured to, in response to the access request 5106, provide aprovable access 5108 to the serverless code logic. In certainembodiments, the controller 4502 provides provable access 5108 byrecording a value on the distributed ledger 4004, providing a user 4009with a token demonstrating access, and/or records a transaction 4510 onthe distributed ledger 4004 indicating the access. In certainembodiments, the serverless code logic 5104 may include logicaldescriptions, computer readable and executable code in any formincluding source code and/or assembly language, and/or a black boxexecutable code embodying the serverless code logic. In certainembodiments, the serverless code logic 5104 further includes, and/or iscommunicated with, an interface description 5118 (e.g., providinginterface parameters; formatting; and/or parameter descriptionsincluding required values, optional values, flags or enable/disablesettings for features, and/or may further include or be communicatedwith documentation, instructions, or the like for the serverless codelogic 5104. In certain embodiments, the interface description 5118 maybe provided as an API to the serverless code logic 5104, and/or may beprovided to support an API implementation to access the serverless codelogic 5104.

An example controller 4502 further interprets an execution operation5112 of the serverless code logic 5104, and records a transaction 4510on the distributed ledger 4004 in response to the execution operation5112. In certain embodiments, interpreting an execution operation 5112of the serverless code logic includes determining that a user hasaccessed the serverless code logic 5104, determining that a user, thecontroller, or another aspect of the transaction-enabling system 5100has provided execution instructions to a device responsive to theserverless code logic 5104, and/or receiving a confirmation, data value,or other communication indicating that the serverless code logic 5104has been executed, downloaded, and/or copied. In certain embodiments,one or more serverless code logic elements stored on the distributedledger 4004 may be at least partially stored in a separate data store5116, where the distributed ledger 4004 may store references, partialinstructions, documentation, interface descriptions 5118, summaries, orthe like, and access the separate data store 5116 of serverless codelogic elements as needed. In certain embodiments, one or more serverlesscode logic 5104 elements may be stored on the distributed ledger 4004.In certain embodiments, access to the serverless code logic 5104elements may be provided in accordance with one or more contract terms4006, and/or may be provided in response to committing a user oraccessing entity to the one or more contract terms 4006.

Referencing FIG. 52, an example procedure 5200 includes an operation5202 to access a distributed ledger, an operation 5204 to tokenizeserverless code logic on the distributed ledger, and an operation 5206to interpret an access request for the serverless code logic. Theexample procedure 5200 includes an operation 5208, in response to theaccess request, to provide a provable access to the serverless codelogic. An example procedure 5200 further includes an operation 5210 toprovide an API for the serverless code logic, and/or an operation 5212to record a transaction on the distributed ledger in response to anaccess operation and/or an execution operation of the serverless codelogic.

Referencing FIG. 53, an example transaction-enabling system 5300includes a controller 4502, where the controller 4502 is configured toaccess a distributed ledger 4004 including an aggregated data set 5304,to interpret an access request 5306 for the aggregated data set, and, inresponse to the access request 5306, to provide a provable access 5308to the aggregated data set 5304. In certain embodiments, the provableaccess 5308 includes which parties have accessed the aggregated dataset, how many parties have accessed the aggregated data set 5304, howmany times each party has accessed the aggregated data set 5304, and/orwhich portions of the aggregated data set 5304 have been accessed. Theaggregated data set 5304 may be stored fully or partially on thedistributed ledger 4004, and/or may reference all or a portion of theaggregated data set 5304 on a separate data store 5316.

In certain embodiments, the distributed ledger 4004 includes a blockchain, and in certain further embodiments the aggregated data set 5304includes a trade secret and/or proprietary information. In certainembodiments, the system 5300 includes an expert wrapper (e.g., operatedby controller 4502) for the distributed ledger 4004, where the expertwrapper tokenizes the aggregated data set 5304 and/or validates a tradesecret and/or proprietary information of the aggregated data set 5304.In certain embodiments, the distributed ledger 4004 includes a set ofinstructions (e.g., as part of the aggregated data set 5304, and/or as aseparate data store on or in communication with the distributed ledger4004), and the controller 4502 is further configured to interpret aninstruction update value, and to update the set of instructions inresponse to the access request 5306 and/or the instruction update value.In certain embodiments, the updated set of instructions are updated onthe distributed ledger 4004, and/or further updated by pushing theupdated instruction set to a user or previous accessor of theinstruction set(s). In certain embodiments, the system 5300 furtherincludes a smart wrapper for the distributed ledger (e.g., operated bythe controller 4502), where the smart wrapper is configured to allocatea number of sub-sets of instructions to the distributed ledger 4004 asthe aggregated data set 5304, and to manage access to the number ofsub-sets of instructions in response to the access request 5306. Incertain further embodiments, the controller 4502 is further configuredto interpret an access 5312 (e.g., by receiving and/or responding to theaccess request 5306), and to record a transaction 4510 on thedistributed ledger 4004 in response to the access. In certainembodiments, the controller 4502 is configured to interpret an execution5312 of one of the number of sub-sets of instructions (e.g., bydetermining executable instructions have been accessed, by providing acommand to a production tool or industrial component in response to theaccess request 5306, and/or via any other execution determinationsdescribed throughout the present disclosure). In certain furtherembodiments, the controller 4502 is further configured to record atransaction 4510 on the distributed ledger 4004 in response to theexecution 5312 of the one of the number of sub-sets of instructions.

Referencing FIG. 54, an example procedure 5400 includes an operation5402 to access a distributed ledger including an aggregated data set, anoperation 5404 to tokenize the aggregated data set to validateinformation (e.g., trade secret or proprietary information) of theaggregated data set, and an operation 5406 to interpret an accessrequest for the aggregated data set. The example procedure 5400 furtherincludes an operation 5408 to provide provable access to the aggregateddata set. In certain embodiments, operation 5408 to provide provableaccess to the aggregated data set includes determining which partieshave accessed the aggregated data set, how many parties have accessedthe aggregated data set, how many times specific parties have accessedthe aggregated data set, and/or combinations of these with portions ofthe aggregated data set. In certain embodiments, the aggregated data setmay include a number of sub-sets of instructions, and the procedure 5400may further include interpreting an update to one of the sub-sets ofinstructions, updating the aggregated data set in response to theupdate(s), and/or pushing an update to a user and/or a previous accessorof the instructions. In certain embodiments, the procedure 5400 includesoperating an expert wrapper to tokenize the aggregated data set and tovalidate the data of the aggregated data set. In certain embodiments,the procedure 5400 includes operating a smart wrapper to manage accessto the aggregated data set. In certain embodiments, the procedure 5400includes an operation 5410 to record a transaction on the distributedledger in response to an access of the aggregated data set, an executionoperation of at least a portion of the aggregated data set, and/or anupdate operation related to the aggregated data set.

Referencing FIG. 55, an example transaction-enabling system 5500includes a controller 4502, where the controller 4502 is configured toaccess a distributed ledger 4004 including intellectual property (IP)data 5504 corresponding to a number of IP assets 5516, where the numberof IP assets include an aggregate stack of IP data 5504. The controller4502 is further configured to tokenize the IP data, to interpret adistributed ledger operation 5506 corresponding to at least one of thenumber of IP assets 5516, and to determine an analytic result value 5512in response to the distributed ledger operation 5506 and the tokenizedIP data, and provide a report of the analytic result value 5512. Incertain embodiments, the IP assets 5516 are the aggregate stack of IP,and in certain embodiments, the IP data 5504 include a list defining theaggregate stack of IP. In certain embodiments, one or more IP assets5516 may be stored within the IP data 5504, and/or may be referencedwithin the IP data 5504 and stored separately (within the distributedledger 4004 or on a IP asset 5516 data store in communication with thedistributed ledger 4004 and/or a wrapper for the distributed ledger4004).

Example and non-limiting distributed ledger operations 5506 includeoperations such as: accessing IP data 5504; executing a processutilizing IP data 5504; adding IP data 5504 corresponding to anadditional IP asset to the aggregate stack of IP; and/or removing IPdata 5504 corresponding from the aggregate stack of IP. In certainfurther embodiments, distributed ledger operations 5506 include one ormore operations such as: changing an owner of an IP asset 5516;installing or executing firmware or executable logic corresponding to IPdata 5504; and/or discontinuing access to the IP data.

Example and non-limiting analytic result values 5512 include resultvalues such as: a number of access events corresponding to at least oneof the plurality of IP assets 5516; statistical informationcorresponding to access events for one or more IP assets 5516; adistribution, frequency, or other description of access events for theIP assets 5516; a distribution, frequency, or other description ofinstallation or execution events for the IP assets; access times and/orprocessing times corresponding to one or more of the IP assets; and/orunique entity access, execution, or installation events for one or moreof the IP assets. In certain embodiments, analytic result values 5512include summaries, statistical analyses (e.g., averages, groupings,determination of outliers, etc.), ordering (e.g., high-to-low volumeaccess rates, revenue values, etc.), timing (e.g., time since a mostrecent access, installation, execution, or update), bucketingdescriptions (e.g., monthly, weekly, by volume or revenue category,etc.) of any of the foregoing, and/or trending descriptions of any ofthe foregoing.

Referencing FIG. 56, an example procedure 5600 includes an operation5602 to access a distributed ledger including a number of IP datacorresponding to a number of IP assets, wherein the number of IP assetsinclude an aggregate stack of IP, an operation 5604 to tokenize the IPdata, and an operation 5606 to interpret distributed ledger operation(s)corresponding to at least one of the plurality of IP assets. The exampleprocedure 5600 further includes an operation 5608 to determine ananalytic result value in response to the distributed ledger operation(s)and the tokenized IP data, and an operation 5610 to provide a report ofthe analytic result value. In certain embodiments, the operation 5610includes providing the report to a user, an entity owning at least oneof the IP assets, an entity considering a purchase of access to at leastone of the IP assets, an operator or administrator for a controller 4502(and/or a smart wrapper for the distributed ledger) and/or thedistributed ledger, and/or a support professional related to thecontroller 4502, the distributed ledger, or the IP assets (e.g., anaccounting professional, a tax professional, an IT support professional,a server administrator, an IP portfolio manager, a regulatory officer,etc.). The information within the report, the type of report available,and/or the underlying set of distributed ledger operations and/orrelated IP assets utilized to inform the report may be restricted and/ormay default to certain parameters depending upon the target entity forthe report and/or the entity requesting the report. In certainembodiments, the procedure 5600 may further include recording atransaction on the distributed ledger in response to the operation 5610to provide the report—for example to record the instance of the reportoccurring and/or the related parameters for the report, to provide aprovable record that the report was provided, and/or to provide for atransaction and/or payment for the report (e.g., providing additionalinformation to a customer, potential customer, and/or asset owner).

In embodiments, provided herein is a transaction-enabling system havinga smart contract wrapper using a distributed ledger wherein the smartcontract embeds IP licensing terms for intellectual property embedded inthe distributed ledger and wherein executing an operation on thedistributed ledger provides access to the intellectual property andcommits the executing party to the IP licensing terms. Certain furtheraspects of the example transaction-enabling system are describedfollowing, any one or more of which may be present in certainembodiments: the system having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property;and the system having a distributed ledger for aggregating intellectualproperty licensing terms, wherein a smart contract wrapper on thedistributed ledger allows an operation on the ledger to add intellectualproperty to agree to an apportionment of royalties among the parties inthe ledger; and the system having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property;and the system having a distributed ledger for aggregating intellectualproperty licensing terms, wherein a smart contract wrapper on thedistributed ledger allows an operation on the ledger to commit a partyto a contract term; and the system having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set; and the systemhaving a distributed ledger that tokenizes executable algorithmic logic,such that operation on the distributed ledger provides provable accessto the executable algorithmic logic; and the system having a distributedledger that tokenizes a 3D printer instruction set, such that operationon the distributed ledger provides provable access to the instructionset; and the system having a distributed ledger that tokenizes aninstruction set for a coating process, such that operation on thedistributed ledger provides provable access to the instruction set; andthe system having a distributed ledger that tokenizes an instruction setfor a semiconductor fabrication process, such that operation on thedistributed ledger provides provable access to the fabrication process;and the system having a distributed ledger that tokenizes a firmwareprogram, such that operation on the distributed ledger provides provableaccess to the firmware program; the system having a distributed ledgerthat tokenizes an instruction set for an FPGA, such that operation onthe distributed ledger provides provable access to the FPGA; the systemhaving a distributed ledger that tokenizes serverless code logic, suchthat operation on the distributed ledger provides provable access to theserverless code logic; the system having a distributed ledger thattokenizes an instruction set for a crystal fabrication system, such thatoperation on the distributed ledger provides provable access to theinstruction set; the system having a distributed ledger that tokenizesan instruction set for a food preparation process, such that operationon the distributed ledger provides provable access to the instructionset; the system having a distributed ledger that tokenizes aninstruction set for a polymer production process, such that operation onthe distributed ledger provides provable access to the instruction set;the system having a distributed ledger that tokenizes an instruction setfor chemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set; the systemhaving a distributed ledger that tokenizes an instruction set for abiological production process, such that operation on the distributedledger provides provable access to the instruction set; the systemhaving a distributed ledger that tokenizes a trade secret with an expertwrapper, such that operation on the distributed ledger provides provableaccess to the trade secret and the wrapper provides validation of thetrade secret by the expert; the system having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret; the system having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger; the system havinga distributed ledger that tokenizes an item of intellectual property anda reporting system that reports an analytic result based on theoperations performed on the distributed ledger or the intellectualproperty; the system having a distributed ledger that aggregates a setof instructions, where an operation on the distributed ledger adds atleast one instruction to a pre-existing set of instructions to provide amodified set of instructions; the system having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets; thesystem having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location; the system having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment; thesystem having an expert system that uses machine learning to improveand/or optimize the execution of cryptocurrency transactions based ontax status; the system having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information; the system having an expert system that usesmachine learning to optimize and/or improve the execution of acryptocurrency transaction based on real time energy price informationfor an available energy source; the system having an expert system thatuses machine learning to optimize and/or improve the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction; thesystem having an expert system that uses machine learning to optimizeand/or improve charging and recharging cycle of a rechargeable batterysystem to provide energy for execution of a cryptocurrency transaction;the system having an expert system that predicts a forward market pricein a market based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction; the system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction; the system having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a cryptocurrency transaction based on the forward marketprediction; the system having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingsocial network data sources and executes a cryptocurrency transactionbased on the forward market prediction; the system having an expertsystem that predicts a forward market price in an energy market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction; the system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction; the system having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources; thesystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected fromautomated agent behavioral data sources; the system having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from automated agent behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from automated agent behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from business entitybehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources; thesystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from businessentity behavioral data sources; the system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources;the system having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from humanbehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from human behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from human behavioral data sources; the system having anexpert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction; the system having an intelligent agent thatis configured to solicit the attention resources of another externalintelligent agent; the system having a machine that automaticallypurchases attention resources in a forward market for attention; thesystem having a fleet of machines that automatically aggregatepurchasing in a forward market for attention; the system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome; the system having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a facility production outcome; the systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize and/orimprove provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles; the system having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource outputselection among a set of available outputs; the system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize requisition andprovisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles; the system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles; the system having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize selection and configuration of an artificialintelligence system to produce a favorable facility output profile amonga set of available artificial intelligence systems and configurations;the system having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility; the system havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to at least one of an input resource, a facilityresource, an output parameter and an external condition related to theoutput of the facility; the system having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to a setof input resources; the system having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources; the system having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to an output parameter; the systemhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a utilization parameter for the output of thefacility; and/or the system having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of parameters received from a digital twin for thefacility.

In embodiments, provided herein is a transaction-enabling system havinga distributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to an aggregatestack of intellectual property. Certain further aspects of the exampletransaction-enabling system are described following, any one or more ofwhich may be present in certain embodiments: the system having adistributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to agree to anapportionment of royalties among the parties in the ledger; the systemhaving a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toan aggregate stack of intellectual property; the system having adistributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to commit a party to a contract term; thesystem having a distributed ledger that tokenizes an instruction set,such that operation on the distributed ledger provides provable accessto the instruction set; the system having a distributed ledger thattokenizes executable algorithmic logic, such that operation on thedistributed ledger provides provable access to the executablealgorithmic logic; the system having a distributed ledger that tokenizesa 3D printer instruction set, such that operation on the distributedledger provides provable access to the instruction set; the systemhaving a distributed ledger that tokenizes an instruction set for acoating process, such that operation on the distributed ledger providesprovable access to the instruction set; the system having a distributedledger that tokenizes an instruction set for a semiconductor fabricationprocess, such that operation on the distributed ledger provides provableaccess to the fabrication process; the system having a distributedledger that tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program; thesystem having a distributed ledger that tokenizes an instruction set foran FPGA, such that operation on the distributed ledger provides provableaccess to the FPGA; the system having a distributed ledger thattokenizes serverless code logic, such that operation on the distributedledger provides provable access to the serverless code logic; the systemhaving a distributed ledger that tokenizes an instruction set for acrystal fabrication system, such that operation on the distributedledger provides provable access to the instruction set; the systemhaving a distributed ledger that tokenizes an instruction set for a foodpreparation process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for a polymerproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes a trade secret with an expert wrapper,such that operation on the distributed ledger provides provable accessto the trade secret and the wrapper provides validation of the tradesecret by the expert; the system having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret; the system having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger; the system havinga distributed ledger that tokenizes an item of intellectual property anda reporting system that reports an analytic result based on theoperations performed on the distributed ledger or the intellectualproperty; the system having a distributed ledger that aggregates a setof instructions, where an operation on the distributed ledger adds atleast one instruction to a pre-existing set of instructions to provide amodified set of instructions; the system having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets; thesystem having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location; the system having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment; thesystem having an expert system that uses machine learning to optimizeand/or improve the execution of cryptocurrency transactions based on taxstatus; the system having an expert system that aggregates regulatoryinformation covering cryptocurrency transactions and automaticallyselects a jurisdiction for an operation based on the regulatoryinformation; the system having an expert system that uses machinelearning to optimize and/or improve the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source; the system having an expert system that uses machinelearning to optimize and/or improve the execution of a cryptocurrencytransaction based on an understanding of available energy sources topower computing resources to execute the transaction; the system havingan expert system that uses machine learning to optimize and/or improvecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction; the systemhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction; the system having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a cryptocurrency transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction; the system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction; the system having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property andhaving an expert system that predicts a forward market price in a marketfor advertising based on an understanding obtained by analyzing Internetof Things data sources and executes a transaction based on the forwardmarket prediction; the system having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction; thesystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from automatedagent behavioral data sources; the system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from automated agent behavioral data sources;the system having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from automatedagent behavioral data sources; the system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from automated agent behavioral data sources;the system having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from businessentity behavioral data sources; the system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from business entity behavioral data sources;the system having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from businessentity behavioral data sources; the system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources;the system having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from humanbehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from human behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property and having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing social data sources andexecutes a transaction based on the forward market prediction; thesystem having an intelligent agent that is configured to solicit theattention resources of another external intelligent agent; the systemhaving a machine that automatically purchases attention resources in aforward market for attention; the system having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention;the system having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to predict alikelihood of a facility production outcome; the system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome; the system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize and/or improve provisioning and allocation of energy andcompute resources to produce a favorable facility resource utilizationprofile among a set of available profiles; the system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize and/or improveprovisioning and allocation of energy and compute resources to produce afavorable facility resource output selection among a set of availableoutputs; the system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize and/or improve requisition and provisioning of available energyand compute resources to produce a favorable facility input resourceprofile among a set of available profiles; the system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize and/or improveconfiguration of available energy and compute resources to produce afavorable facility resource configuration profile among a set ofavailable profiles; the system having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize and/or improve selection and configurationof an artificial intelligence system to produce a favorable facilityoutput profile among a set of available artificial intelligence systemsand configurations; the system having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to generate an indication that a current or prospectivecustomer should be contacted about an output that can be provided by thefacility; the system having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility; the system having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of input resources; the system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of facility resources; the system having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter; the system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility;and/or the system having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of parameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga distributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to agree to anapportionment of royalties among the parties in the ledger. Certainfurther aspects of the example transaction-enabling system are describedfollowing, any one or more of which may be present in certainembodiments: the system having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property;the system having a distributed ledger for aggregating intellectualproperty licensing terms, wherein a smart contract wrapper on thedistributed ledger allows an operation on the ledger to commit a partyto a contract term; the system having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set; the systemhaving a distributed ledger that tokenizes executable algorithmic logic,such that operation on the distributed ledger provides provable accessto the executable algorithmic logic; the system having a distributedledger that tokenizes a 3D printer instruction set, such that operationon the distributed ledger provides provable access to the instructionset; the system having a distributed ledger that tokenizes aninstruction set for a coating process, such that operation on thedistributed ledger provides provable access to the instruction set; thesystem having a distributed ledger that tokenizes an instruction set fora semiconductor fabrication process, such that operation on thedistributed ledger provides provable access to the fabrication process;the system having a distributed ledger that tokenizes a firmwareprogram, such that operation on the distributed ledger provides provableaccess to the firmware program; the system having a distributed ledgerthat tokenizes an instruction set for an FPGA, such that operation onthe distributed ledger provides provable access to the FPGA; the systemhaving a distributed ledger that tokenizes serverless code logic, suchthat operation on the distributed ledger provides provable access to theserverless code logic; the system having a distributed ledger thattokenizes an instruction set for a crystal fabrication system, such thatoperation on the distributed ledger provides provable access to theinstruction set; the system having a distributed ledger that tokenizesan instruction set for a food preparation process, such that operationon the distributed ledger provides provable access to the instructionset; the system having a distributed ledger that tokenizes aninstruction set for a polymer production process, such that operation onthe distributed ledger provides provable access to the instruction set;the system having a distributed ledger that tokenizes an instruction setfor chemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set; the systemhaving a distributed ledger that tokenizes an instruction set for abiological production process, such that operation on the distributedledger provides provable access to the instruction set; the systemhaving a distributed ledger that tokenizes a trade secret with an expertwrapper, such that operation on the distributed ledger provides provableaccess to the trade secret and the wrapper provides validation of thetrade secret by the expert; the system having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret; the system having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger; the system havinga distributed ledger that tokenizes an item of intellectual property anda reporting system that reports an analytic result based on theoperations performed on the distributed ledger or the intellectualproperty; the system having a distributed ledger that aggregates a setof instructions, where an operation on the distributed ledger adds atleast one instruction to a pre-existing set of instructions to provide amodified set of instructions; the system having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets; thesystem having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location; the system having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment; thesystem having an expert system that uses machine learning to optimizeand/or improve the execution of cryptocurrency transactions based on taxstatus; the system having an expert system that aggregates regulatoryinformation covering cryptocurrency transactions and automaticallyselects a jurisdiction for an operation based on the regulatoryinformation; the system having an expert system that uses machinelearning to optimize and/or improve the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source; the system having an expert system that uses machinelearning to optimize and/or improve the execution of a cryptocurrencytransaction based on an understanding of available energy sources topower computing resources to execute the transaction; the system havingan expert system that uses machine learning to optimize and/or improvecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction; the systemhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction; the system having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a cryptocurrency transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction; the system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction; the system having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources; thesystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected fromautomated agent behavioral data sources; the system having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from automated agent behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from automated agent behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from business entitybehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources; thesystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from businessentity behavioral data sources; the system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources;the system having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from humanbehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from human behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from human behavioral data sources; the system having anexpert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction; the system having an intelligent agent thatis configured to solicit the attention resources of another externalintelligent agent; the system having a machine that automaticallypurchases attention resources in a forward market for attention; thesystem having a fleet of machines that automatically aggregatepurchasing in a forward market for attention; the system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome; the system having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a facility production outcome; the systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize and/orimprove provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles; the system having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize and/or improve provisioning and allocationof energy and compute resources to produce a favorable facility resourceoutput selection among a set of available outputs; the system having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize and/or improverequisition and provisioning of available energy and compute resourcesto produce a favorable facility input resource profile among a set ofavailable profiles; the system having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize and/or improve configuration of availableenergy and compute resources to produce a favorable facility resourceconfiguration profile among a set of available profiles; the systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize and/orimprove selection and configuration of an artificial intelligence systemto produce a favorable facility output profile among a set of availableartificial intelligence systems and configurations; the system having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility; the system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility; the system having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources; thesystem having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources; the systemhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter; the system having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility;and/or the system having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of parameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga distributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to an aggregatestack of intellectual property. Certain further aspects of the exampletransaction-enabling system are described following, any one or more ofwhich may be present in certain embodiments: the system having adistributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to commit a party to a contract term; thesystem having a distributed ledger that tokenizes an instruction set,such that operation on the distributed ledger provides provable accessto the instruction set; the system having a distributed ledger thattokenizes executable algorithmic logic, such that operation on thedistributed ledger provides provable access to the executablealgorithmic logic; the system having a distributed ledger that tokenizesa 3D printer instruction set, such that operation on the distributedledger provides provable access to the instruction set; the systemhaving a distributed ledger that tokenizes an instruction set for acoating process, such that operation on the distributed ledger providesprovable access to the instruction set; the system having a distributedledger that tokenizes an instruction set for a semiconductor fabricationprocess, such that operation on the distributed ledger provides provableaccess to the fabrication process; the system having a distributedledger that tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program; thesystem having a distributed ledger that tokenizes an instruction set foran FPGA, such that operation on the distributed ledger provides provableaccess to the FPGA; the system having a distributed ledger thattokenizes serverless code logic, such that operation on the distributedledger provides provable access to the serverless code logic; the systemhaving a distributed ledger that tokenizes an instruction set for acrystal fabrication system, such that operation on the distributedledger provides provable access to the instruction set; the systemhaving a distributed ledger that tokenizes an instruction set for a foodpreparation process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for a polymerproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes a trade secret with an expert wrapper,such that operation on the distributed ledger provides provable accessto the trade secret and the wrapper provides validation of the tradesecret by the expert; the system having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret; the system having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger; the system havinga distributed ledger that tokenizes an item of intellectual property anda reporting system that reports an analytic result based on theoperations performed on the distributed ledger or the intellectualproperty; the system having a distributed ledger that aggregates a setof instructions, where an operation on the distributed ledger adds atleast one instruction to a pre-existing set of instructions to provide amodified set of instructions; the system having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets; thesystem having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location; the system having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment; thesystem having an expert system that uses machine learning to optimizeand/or improve the execution of cryptocurrency transactions based on taxstatus; the system having an expert system that aggregates regulatoryinformation covering cryptocurrency transactions and automaticallyselects a jurisdiction for an operation based on the regulatoryinformation; the system having an expert system that uses machinelearning to optimize and/or improve the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source; the system having an expert system that uses machinelearning to optimize and/or improve the execution of a cryptocurrencytransaction based on an understanding of available energy sources topower computing resources to execute the transaction; the system havingan expert system that uses machine learning to optimize and/or improvecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction; the systemhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction; the system having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a cryptocurrency transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction; the system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction; the system having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources; thesystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected fromautomated agent behavioral data sources; the system having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from automated agent behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from automated agent behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from business entitybehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources; thesystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from businessentity behavioral data sources; the system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources;the system having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from humanbehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from human behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from human behavioral data sources; the system having anexpert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction; the system having an intelligent agent thatis configured to solicit the attention resources of another externalintelligent agent; the system having a machine that automaticallypurchases attention resources in a forward market for attention; thesystem having a fleet of machines that automatically aggregatepurchasing in a forward market for attention; the system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome; the system having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a facility production outcome; the systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize and/orimprove provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles; the system having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize and/or improve provisioning and allocationof energy and compute resources to produce a favorable facility resourceoutput selection among a set of available outputs; the system having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize and/or improverequisition and provisioning of available energy and compute resourcesto produce a favorable facility input resource profile among a set ofavailable profiles; the system having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize and/or improve configuration of availableenergy and compute resources to produce a favorable facility resourceconfiguration profile among a set of available profiles; the systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize and/orimprove selection and configuration of an artificial intelligence systemto produce a favorable facility output profile among a set of availableartificial intelligence systems and configurations; the system having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility; the system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility; the system having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources; thesystem having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources; the systemhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter; the system having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility;and/or the system having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of parameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga distributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to commit a party to a contract term. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to commit a party to a contract term andhaving a distributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set; the system having a distributed ledger that tokenizesexecutable algorithmic logic, such that operation on the distributedledger provides provable access to the executable algorithmic logic; thesystem having a distributed ledger that tokenizes a 3D printerinstruction set, such that operation on the distributed ledger providesprovable access to the instruction set; the system having a distributedledger that tokenizes an instruction set for a coating process, suchthat operation on the distributed ledger provides provable access to theinstruction set; the system having a distributed ledger that tokenizesan instruction set for a semiconductor fabrication process, such thatoperation on the distributed ledger provides provable access to thefabrication process; the system having a distributed ledger thattokenizes a firmware program, such that operation on the distributedledger provides provable access to the firmware program; the systemhaving a distributed ledger that tokenizes an instruction set for anFPGA, such that operation on the distributed ledger provides provableaccess to the FPGA; the system having a distributed ledger thattokenizes serverless code logic, such that operation on the distributedledger provides provable access to the serverless code logic; the systemhaving a distributed ledger that tokenizes an instruction set for acrystal fabrication system, such that operation on the distributedledger provides provable access to the instruction set; the systemhaving a distributed ledger that tokenizes an instruction set for a foodpreparation process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for a polymerproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes a trade secret with an expert wrapper,such that operation on the distributed ledger provides provable accessto the trade secret and the wrapper provides validation of the tradesecret by the expert; the system having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret; the system having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger; the system havinga distributed ledger that tokenizes an item of intellectual property anda reporting system that reports an analytic result based on theoperations performed on the distributed ledger or the intellectualproperty; the system having a distributed ledger that aggregates a setof instructions, where an operation on the distributed ledger adds atleast one instruction to a pre-existing set of instructions to provide amodified set of instructions; the system having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets; thesystem having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location; the system having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment; thesystem having an expert system that uses machine learning to optimizeand/or improve the execution of cryptocurrency transactions based on taxstatus; the system having an expert system that aggregates regulatoryinformation covering cryptocurrency transactions and automaticallyselects a jurisdiction for an operation based on the regulatoryinformation; the system having an expert system that uses machinelearning to optimize and/or improve the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source; the system having an expert system that uses machinelearning to optimize and/or improve the execution of a cryptocurrencytransaction based on an understanding of available energy sources topower computing resources to execute the transaction; the system havingan expert system that uses machine learning to optimize and/or improvecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction; the systemhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction; the system having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a cryptocurrency transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction; the system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction; the system having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources; thesystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected fromautomated agent behavioral data sources; the system having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from automated agent behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from automated agent behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from business entitybehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources; thesystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from businessentity behavioral data sources; the system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources;the system having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from humanbehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from human behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from human behavioral data sources; the system having anexpert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction; the system having an intelligent agent thatis configured to solicit the attention resources of another externalintelligent agent; the system having a machine that automaticallypurchases attention resources in a forward market for attention; thesystem having a fleet of machines that automatically aggregatepurchasing in a forward market for attention; the system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome; the system having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a facility production outcome; the systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize and/orimprove provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles; the system having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize and/or improve provisioning and allocationof energy and compute resources to produce a favorable facility resourceoutput selection among a set of available outputs; the system having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize and/or improverequisition and provisioning of available energy and compute resourcesto produce a favorable facility input resource profile among a set ofavailable profiles; the system having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize and/or improve configuration of availableenergy and compute resources to produce a favorable facility resourceconfiguration profile among a set of available profiles; the systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize and/orimprove selection and configuration of an artificial intelligence systemto produce a favorable facility output profile among a set of availableartificial intelligence systems and configurations; the system having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility; the system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility; the system having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources; thesystem having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources; the systemhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter; the system having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility;and/or the system having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of parameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set. Certain further aspects of the exampletransaction-enabling system are described following, any one or more ofwhich may be present in certain embodiments: the system having adistributed ledger that tokenizes executable algorithmic logic, suchthat operation on the distributed ledger provides provable access to theexecutable algorithmic logic; the system having a distributed ledgerthat tokenizes a 3D printer instruction set, such that operation on thedistributed ledger provides provable access to the instruction set; thesystem having a distributed ledger that tokenizes an instruction set fora coating process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for a semiconductorfabrication process, such that operation on the distributed ledgerprovides provable access to the fabrication process; the system having adistributed ledger that tokenizes a firmware program, such thatoperation on the distributed ledger provides provable access to thefirmware program; the system having a distributed ledger that tokenizesan instruction set for an FPGA, such that operation on the distributedledger provides provable access to the FPGA; the system having adistributed ledger that tokenizes serverless code logic, such thatoperation on the distributed ledger provides provable access to theserverless code logic; the system having a distributed ledger thattokenizes an instruction set for a crystal fabrication system, such thatoperation on the distributed ledger provides provable access to theinstruction set; the system having a distributed ledger that tokenizesan instruction set for a food preparation process, such that operationon the distributed ledger provides provable access to the instructionset; the system having a distributed ledger that tokenizes aninstruction set for a polymer production process, such that operation onthe distributed ledger provides provable access to the instruction set;the system having a distributed ledger that tokenizes an instruction setfor chemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set; the systemhaving a distributed ledger that tokenizes an instruction set for abiological production process, such that operation on the distributedledger provides provable access to the instruction set; the systemhaving a distributed ledger that tokenizes a trade secret with an expertwrapper, such that operation on the distributed ledger provides provableaccess to the trade secret and the wrapper provides validation of thetrade secret by the expert; the system having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret; the system having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger; the system havinga distributed ledger that tokenizes an item of intellectual property anda reporting system that reports an analytic result based on theoperations performed on the distributed ledger or the intellectualproperty; the system having a distributed ledger that aggregates a setof instructions, where an operation on the distributed ledger adds atleast one instruction to a pre-existing set of instructions to provide amodified set of instructions; the system having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets; thesystem having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location; the system having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment; thesystem having an expert system that uses machine learning to optimizeand/or improve the execution of cryptocurrency transactions based on taxstatus; the system having an expert system that aggregates regulatoryinformation covering cryptocurrency transactions and automaticallyselects a jurisdiction for an operation based on the regulatoryinformation; the system having an expert system that uses machinelearning to optimize and/or improve the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source; the system having an expert system that uses machinelearning to optimize and/or improve the execution of a cryptocurrencytransaction based on an understanding of available energy sources topower computing resources to execute the transaction; the system havingan expert system that uses machine learning to optimize and/or improvecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction; the systemhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction; the system having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a cryptocurrency transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction; the system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction; the system having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources; thesystem having a distributed ledger that tokenizes an instruction set,such that operation on the distributed ledger provides provable accessto the instruction set and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from automated agent behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from automated agentbehavioral data sources; the system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from automated agent behavioral data sources; thesystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from businessentity behavioral data sources; the system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from business entity behavioral data sources;the system having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from businessentity behavioral data sources; the system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources;the system having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from humanbehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from human behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from human behavioral data sources; the system having anexpert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction; the system having an intelligent agent thatis configured to solicit the attention resources of another externalintelligent agent; the system having a machine that automaticallypurchases attention resources in a forward market for attention; thesystem having a fleet of machines that automatically aggregatepurchasing in a forward market for attention; the system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome; the system having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a facility production outcome; the systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize and/orimprove provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles; the system having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize and/or improve provisioning and allocationof energy and compute resources to produce a favorable facility resourceoutput selection among a set of available outputs; the system having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize and/or improverequisition and provisioning of available energy and compute resourcesto produce a favorable facility input resource profile among a set ofavailable profiles; the system having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize and/or improve configuration of availableenergy and compute resources to produce a favorable facility resourceconfiguration profile among a set of available profiles; the systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize and/orimprove selection and configuration of an artificial intelligence systemto produce a favorable facility output profile among a set of availableartificial intelligence systems and configurations; the system having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility; the system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility; the system having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources; thesystem having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources; the systemhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter; the system having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility;and/or the system having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of parameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes executable algorithmic logic, suchthat operation on the distributed ledger provides provable access to theexecutable algorithmic logic. Certain further aspects of the exampletransaction-enabling system are described following, any one or more ofwhich may be present in certain embodiments: the system having adistributed ledger that tokenizes a 3D printer instruction set, suchthat operation on the distributed ledger provides provable access to theinstruction set; the system having a distributed ledger that tokenizesan instruction set for a coating process, such that operation on thedistributed ledger provides provable access to the instruction set; thesystem having a distributed ledger that tokenizes an instruction set fora semiconductor fabrication process, such that operation on thedistributed ledger provides provable access to the fabrication process;the system having a distributed ledger that tokenizes a firmwareprogram, such that operation on the distributed ledger provides provableaccess to the firmware program; the system having a distributed ledgerthat tokenizes an instruction set for an FPGA, such that operation onthe distributed ledger provides provable access to the FPGA; the systemhaving a distributed ledger that tokenizes serverless code logic, suchthat operation on the distributed ledger provides provable access to theserverless code logic; the system having a distributed ledger thattokenizes an instruction set for a crystal fabrication system, such thatoperation on the distributed ledger provides provable access to theinstruction set; the system having a distributed ledger that tokenizesan instruction set for a food preparation process, such that operationon the distributed ledger provides provable access to the instructionset; the system having a distributed ledger that tokenizes aninstruction set for a polymer production process, such that operation onthe distributed ledger provides provable access to the instruction set;the system having a distributed ledger that tokenizes an instruction setfor chemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set; the systemhaving a distributed ledger that tokenizes an instruction set for abiological production process, such that operation on the distributedledger provides provable access to the instruction set; the systemhaving a distributed ledger that tokenizes a trade secret with an expertwrapper, such that operation on the distributed ledger provides provableaccess to the trade secret and the wrapper provides validation of thetrade secret by the expert; the system having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret; the system having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger; the system havinga distributed ledger that tokenizes an item of intellectual property anda reporting system that reports an analytic result based on theoperations performed on the distributed ledger or the intellectualproperty; the system having a distributed ledger that aggregates a setof instructions, where an operation on the distributed ledger adds atleast one instruction to a pre-existing set of instructions to provide amodified set of instructions; the system having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets; thesystem having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location; the system having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment; thesystem having an expert system that uses machine learning to optimizeand/or improve the execution of cryptocurrency transactions based on taxstatus; the system having an expert system that aggregates regulatoryinformation covering cryptocurrency transactions and automaticallyselects a jurisdiction for an operation based on the regulatoryinformation; the system having an expert system that uses machinelearning to optimize and/or improve the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source; the system having an expert system that uses machinelearning to optimize and/or improve the execution of a cryptocurrencytransaction based on an understanding of available energy sources topower computing resources to execute the transaction; the system havingan expert system that uses machine learning to optimize and/or improvecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction; the systemhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction; the system having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a cryptocurrency transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction; the system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction; the system having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources; thesystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected fromautomated agent behavioral data sources; the system having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from automated agent behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from automated agent behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from business entitybehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources; thesystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from businessentity behavioral data sources; the system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources;the system having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from humanbehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from human behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from human behavioral data sources; the system having anexpert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction; the system having an intelligent agent thatis configured to solicit the attention resources of another externalintelligent agent; the system having a machine that automaticallypurchases attention resources in a forward market for attention; thesystem having a fleet of machines that automatically aggregatepurchasing in a forward market for attention; the system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome; the system having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a facility production outcome; the systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize and/orimprove provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles; the system having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize and/or improve provisioning and allocationof energy and compute resources to produce a favorable facility resourceoutput selection among a set of available outputs; the system having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize and/or improverequisition and provisioning of available energy and compute resourcesto produce a favorable facility input resource profile among a set ofavailable profiles; the system having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize and/or improve configuration of availableenergy and compute resources to produce a favorable facility resourceconfiguration profile among a set of available profiles; the systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize and/orimprove selection and configuration of an artificial intelligence systemto produce a favorable facility output profile among a set of availableartificial intelligence systems and configurations; the system having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility; the system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility; the system having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources; thesystem having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources; the systemhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter; the system having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility;and/or the system having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of parameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes a 3D printer instruction set, suchthat operation on the distributed ledger provides provable access to theinstruction set. Certain further aspects of the exampletransaction-enabling system are described following, any one or more ofwhich may be present in certain embodiments: the system having adistributed ledger that tokenizes an instruction set for a coatingprocess, such that operation on the distributed ledger provides provableaccess to the instruction set; the system having a distributed ledgerthat tokenizes an instruction set for a semiconductor fabricationprocess, such that operation on the distributed ledger provides provableaccess to the fabrication process; the system having a distributedledger that tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program; thesystem having a distributed ledger that tokenizes an instruction set foran FPGA, such that operation on the distributed ledger provides provableaccess to the FPGA; the system having a distributed ledger thattokenizes serverless code logic, such that operation on the distributedledger provides provable access to the serverless code logic; the systemhaving a distributed ledger that tokenizes an instruction set for acrystal fabrication system, such that operation on the distributedledger provides provable access to the instruction set; the systemhaving a distributed ledger that tokenizes an instruction set for a foodpreparation process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for a polymerproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes a trade secret with an expert wrapper,such that operation on the distributed ledger provides provable accessto the trade secret and the wrapper provides validation of the tradesecret by the expert; the system having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret; the system having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger; the system havinga distributed ledger that tokenizes an item of intellectual property anda reporting system that reports an analytic result based on theoperations performed on the distributed ledger or the intellectualproperty; the system having a distributed ledger that aggregates a setof instructions, where an operation on the distributed ledger adds atleast one instruction to a pre-existing set of instructions to provide amodified set of instructions; the system having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets; thesystem having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location; the system having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment; thesystem having an expert system that uses machine learning to optimizethe execution of cryptocurrency transactions based on tax status; thesystem having an expert system that aggregates regulatory informationcovering cryptocurrency transactions and automatically selects ajurisdiction for an operation based on the regulatory information; thesystem having an expert system that uses machine learning to optimizethe execution of a cryptocurrency transaction based on real time energyprice information for an available energy source; the system having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction; thesystem having an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction; the systemhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction; the system having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a cryptocurrency transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction; the system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction; the system having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources; thesystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected fromautomated agent behavioral data sources; the system having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from automated agent behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from automated agent behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from business entitybehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources; thesystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from businessentity behavioral data sources; the system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources;the system having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from humanbehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from human behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from human behavioral data sources; the system having anexpert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction; the system having an intelligent agent thatis configured to solicit the attention resources of another externalintelligent agent; the system having a machine that automaticallypurchases attention resources in a forward market for attention; thesystem having a fleet of machines that automatically aggregatepurchasing in a forward market for attention; the system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome; the system having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a facility production outcome; the systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles;the system having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizeprovisioning and allocation of energy and compute resources to produce afavorable facility resource output selection among a set of availableoutputs; the system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles; the system having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles; the system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations; the system having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility; the system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility; the system having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources; thesystem having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources; the systemhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter; the system having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility;and/or the system having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of parameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes an instruction set for a coatingprocess, such that operation on the distributed ledger provides provableaccess to the instruction set. Certain further aspects of the exampletransaction-enabling system are described following, any one or more ofwhich may be present in certain embodiments: the system having adistributed ledger that tokenizes an instruction set for a semiconductorfabrication process, such that operation on the distributed ledgerprovides provable access to the fabrication process; the system having adistributed ledger that tokenizes a firmware program, such thatoperation on the distributed ledger provides provable access to thefirmware program; the system having a distributed ledger that tokenizesan instruction set for an FPGA, such that operation on the distributedledger provides provable access to the FPGA; the system having adistributed ledger that tokenizes serverless code logic, such thatoperation on the distributed ledger provides provable access to theserverless code logic; the system having a distributed ledger thattokenizes an instruction set for a crystal fabrication system, such thatoperation on the distributed ledger provides provable access to theinstruction set; the system having a distributed ledger that tokenizesan instruction set for a food preparation process, such that operationon the distributed ledger provides provable access to the instructionset; the system having a distributed ledger that tokenizes aninstruction set for a polymer production process, such that operation onthe distributed ledger provides provable access to the instruction set;the system having a distributed ledger that tokenizes an instruction setfor chemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set; the systemhaving a distributed ledger that tokenizes an instruction set for abiological production process, such that operation on the distributedledger provides provable access to the instruction set; the systemhaving a distributed ledger that tokenizes a trade secret with an expertwrapper, such that operation on the distributed ledger provides provableaccess to the trade secret and the wrapper provides validation of thetrade secret by the expert; the system having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret; the system having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger; the system havinga distributed ledger that tokenizes an item of intellectual property anda reporting system that reports an analytic result based on theoperations performed on the distributed ledger or the intellectualproperty; the system having a distributed ledger that aggregates a setof instructions, where an operation on the distributed ledger adds atleast one instruction to a pre-existing set of instructions to provide amodified set of instructions; the system having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets; thesystem having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location; the system having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment; thesystem having an expert system that uses machine learning to optimizethe execution of cryptocurrency transactions based on tax status; thesystem having an expert system that aggregates regulatory informationcovering cryptocurrency transactions and automatically selects ajurisdiction for an operation based on the regulatory information; thesystem having an expert system that uses machine learning to optimizethe execution of a cryptocurrency transaction based on real time energyprice information for an available energy source; the system having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction; thesystem having an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction; the systemhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction; the system having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a cryptocurrency transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction; the system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction; the system having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources; thesystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected fromautomated agent behavioral data sources; the system having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from automated agent behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from automated agent behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from business entitybehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources; thesystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from businessentity behavioral data sources; the system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources;the system having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from humanbehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from human behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from human behavioral data sources; the system having anexpert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction; the system having an intelligent agent thatis configured to solicit the attention resources of another externalintelligent agent; the system having a machine that automaticallypurchases attention resources in a forward market for attention; thesystem having a fleet of machines that automatically aggregatepurchasing in a forward market for attention; the system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome; the system having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a facility production outcome; the systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles;the system having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizeprovisioning and allocation of energy and compute resources to produce afavorable facility resource output selection among a set of availableoutputs; the system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles; the system having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles; the system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations; the system having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility; the system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility; the system having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources; thesystem having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources; the systemhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter; the system having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility;and/or the system having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of parameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes an instruction set for asemiconductor fabrication process, such that operation on thedistributed ledger provides provable access to the fabrication process.Certain further aspects of the example transaction-enabling system aredescribed following, any one or more of which may be present in certainembodiments: the system having a distributed ledger that tokenizes afirmware program, such that operation on the distributed ledger providesprovable access to the firmware program; the system having a distributedledger that tokenizes an instruction set for an FPGA, such thatoperation on the distributed ledger provides provable access to theFPGA; the system having a distributed ledger that tokenizes serverlesscode logic, such that operation on the distributed ledger providesprovable access to the serverless code logic; the system having adistributed ledger that tokenizes an instruction set for a crystalfabrication system, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for a foodpreparation process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for a polymerproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes a trade secret with an expert wrapper,such that operation on the distributed ledger provides provable accessto the trade secret and the wrapper provides validation of the tradesecret by the expert; the system having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret; the system having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger; the system havinga distributed ledger that tokenizes an item of intellectual property anda reporting system that reports an analytic result based on theoperations performed on the distributed ledger or the intellectualproperty; the system having a distributed ledger that aggregates a setof instructions, where an operation on the distributed ledger adds atleast one instruction to a pre-existing set of instructions to provide amodified set of instructions; the system having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets; thesystem having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location; the system having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment; thesystem having an expert system that uses machine learning to optimizethe execution of cryptocurrency transactions based on tax status; thesystem having an expert system that aggregates regulatory informationcovering cryptocurrency transactions and automatically selects ajurisdiction for an operation based on the regulatory information; thesystem having an expert system that uses machine learning to optimizethe execution of a cryptocurrency transaction based on real time energyprice information for an available energy source; the system having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction; thesystem having an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction; the systemhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction; the system having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a cryptocurrency transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction; the system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction; the system having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources; thesystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected fromautomated agent behavioral data sources; the system having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from automated agent behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from automated agent behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from business entitybehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources; thesystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from businessentity behavioral data sources; the system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources;the system having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from humanbehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from human behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from human behavioral data sources; the system having anexpert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction; the system having an intelligent agent thatis configured to solicit the attention resources of another externalintelligent agent; the system having a machine that automaticallypurchases attention resources in a forward market for attention; thesystem having a fleet of machines that automatically aggregatepurchasing in a forward market for attention; the system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome; the system having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a facility production outcome; the systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles;the system having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizeprovisioning and allocation of energy and compute resources to produce afavorable facility resource output selection among a set of availableoutputs; the system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles; the system having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles; the system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations; the system having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility; the system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility; the system having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources; thesystem having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources; the systemhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter; the system having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility; thesystem having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofparameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes a firmware program, such thatoperation on the distributed ledger provides provable access to thefirmware program. Certain further aspects of the exampletransaction-enabling system are described following, any one or more ofwhich may be present in certain embodiments: In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes a firmware program, such that operation on the distributedledger provides provable access to the firmware program and having adistributed ledger that tokenizes an instruction set for an FPGA, suchthat operation on the distributed ledger provides provable access to theFPGA. In embodiments, provided herein is a transaction-enabling systemhaving a distributed ledger that tokenizes a firmware program, such thatoperation on the distributed ledger provides provable access to thefirmware program and having a distributed ledger that tokenizesserverless code logic, such that operation on the distributed ledgerprovides provable access to the serverless code logic. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program andhaving a distributed ledger that tokenizes an instruction set for acrystal fabrication system, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program andhaving a distributed ledger that tokenizes an instruction set for a foodpreparation process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program andhaving a distributed ledger that tokenizes an instruction set for apolymer production process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program andhaving a distributed ledger that tokenizes an instruction set forchemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program andhaving a distributed ledger that tokenizes an instruction set for abiological production process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program andhaving a distributed ledger that tokenizes a trade secret with an expertwrapper, such that operation on the distributed ledger provides provableaccess to the trade secret and the wrapper provides validation of thetrade secret by the expert. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes afirmware program, such that operation on the distributed ledger providesprovable access to the firmware program and having a distributed ledgerthat aggregates views of a trade secret into a chain that proves whichand how many parties have viewed the trade secret. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program andhaving a distributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program andhaving a distributed ledger that tokenizes an item of intellectualproperty and a reporting system that reports an analytic result based onthe operations performed on the distributed ledger or the intellectualproperty. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes a firmware program,such that operation on the distributed ledger provides provable accessto the firmware program and having a distributed ledger that aggregatesa set of instructions, where an operation on the distributed ledger addsat least one instruction to a pre-existing set of instructions toprovide a modified set of instructions. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thattokenizes a firmware program, such that operation on the distributedledger provides provable access to the firmware program and having asmart wrapper for management of a distributed ledger that aggregatessets of instructions, where the smart wrapper manages allocation ofinstruction sub-sets to the distributed ledger and access to theinstruction sub-sets. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes afirmware program, such that operation on the distributed ledger providesprovable access to the firmware program and having a smart wrapper for acryptocurrency coin that directs execution of a transaction involvingthe coin to a geographic location based on tax treatment of at least oneof the coin and the transaction in the geographic location. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes a firmware program, such thatoperation on the distributed ledger provides provable access to thefirmware program and having a self-executing cryptocurrency coin thatcommits a transaction upon recognizing a location-based parameter thatprovides favorable tax treatment. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes afirmware program, such that operation on the distributed ledger providesprovable access to the firmware program and having an expert system thatuses machine learning to optimize the execution of cryptocurrencytransactions based on tax status. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes afirmware program, such that operation on the distributed ledger providesprovable access to the firmware program and having an expert system thataggregates regulatory information covering cryptocurrency transactionsand automatically selects a jurisdiction for an operation based on theregulatory information. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes afirmware program, such that operation on the distributed ledger providesprovable access to the firmware program and having an expert system thatuses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes a firmware program,such that operation on the distributed ledger provides provable accessto the firmware program and having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon an understanding of available energy sources to power computingresources to execute the transaction. In embodiments, provided herein isa transaction-enabling system having a distributed ledger that tokenizesa firmware program, such that operation on the distributed ledgerprovides provable access to the firmware program and having an expertsystem that uses machine learning to optimize charging and rechargingcycle of a rechargeable battery system to provide energy for executionof a cryptocurrency transaction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes afirmware program, such that operation on the distributed ledger providesprovable access to the firmware program and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program andhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes a firmware program,such that operation on the distributed ledger provides provable accessto the firmware program and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program andhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing social network datasources and executes a cryptocurrency transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes afirmware program, such that operation on the distributed ledger providesprovable access to the firmware program and having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes a firmware program, such thatoperation on the distributed ledger provides provable access to thefirmware program and having an expert system that predicts a forwardmarket price in an energy market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes afirmware program, such that operation on the distributed ledger providesprovable access to the firmware program and having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes a firmware program,such that operation on the distributed ledger provides provable accessto the firmware program and having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes a firmware program,such that operation on the distributed ledger provides provable accessto the firmware program and having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes a firmware program, such thatoperation on the distributed ledger provides provable access to thefirmware program and having an expert system that predicts a forwardmarket price in a market for advertising based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program andhaving an expert system that predicts a forward market price in a marketfor advertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes afirmware program, such that operation on the distributed ledger providesprovable access to the firmware program and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes a firmware program, such thatoperation on the distributed ledger provides provable access to thefirmware program and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes afirmware program, such that operation on the distributed ledger providesprovable access to the firmware program and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes a firmware program, such thatoperation on the distributed ledger provides provable access to thefirmware program and having a machine that automatically forecastsforward market pricing of energy prices based on information collectedfrom business entity behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes a firmware program, such that operation on the distributedledger provides provable access to the firmware program and having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes a firmware program,such that operation on the distributed ledger provides provable accessto the firmware program and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes afirmware program, such that operation on the distributed ledger providesprovable access to the firmware program and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes a firmware program, such thatoperation on the distributed ledger provides provable access to thefirmware program and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes a firmware program, such that operation on the distributedledger provides provable access to the firmware program and having amachine that automatically forecasts forward market pricing of energycredits based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes a firmware program,such that operation on the distributed ledger provides provable accessto the firmware program and having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes a firmware program, such thatoperation on the distributed ledger provides provable access to thefirmware program and having an expert system that predicts a forwardmarket price in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing social data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program andhaving an intelligent agent that is configured to solicit the attentionresources of another external intelligent agent. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program andhaving a machine that automatically purchases attention resources in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes afirmware program, such that operation on the distributed ledger providesprovable access to the firmware program and having a fleet of machinesthat automatically aggregate purchasing in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes a firmware program,such that operation on the distributed ledger provides provable accessto the firmware program and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto predict a likelihood of a facility production outcome. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes a firmware program, such thatoperation on the distributed ledger provides provable access to thefirmware program and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a facility production outcome. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thattokenizes a firmware program, such that operation on the distributedledger provides provable access to the firmware program and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes a firmware program, such thatoperation on the distributed ledger provides provable access to thefirmware program and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes afirmware program, such that operation on the distributed ledger providesprovable access to the firmware program and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize requisition and provisioning of availableenergy and compute resources to produce a favorable facility inputresource profile among a set of available profiles. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimizeconfiguration of available energy and compute resources to produce afavorable facility resource configuration profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes afirmware program, such that operation on the distributed ledger providesprovable access to the firmware program and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize selection and configuration of an artificialintelligence system to produce a favorable facility output profile amonga set of available artificial intelligence systems and configurations.In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes a firmware program, such thatoperation on the distributed ledger provides provable access to thefirmware program and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes a firmware program, such thatoperation on the distributed ledger provides provable access to thefirmware program and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of input resources. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of facility resources. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes a firmware program, such that operation on the distributedledger provides provable access to the firmware program and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes a firmware program, such thatoperation on the distributed ledger provides provable access to thefirmware program and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of parameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes an instruction set for an FPGA, suchthat operation on the distributed ledger provides provable access to theFPGA. Certain further aspects of the example transaction-enabling systemare described following, any one or more of which may be present incertain embodiments: the system having a distributed ledger thattokenizes serverless code logic, such that operation on the distributedledger provides provable access to the serverless code logic; the systemhaving a distributed ledger that tokenizes an instruction set for acrystal fabrication system, such that operation on the distributedledger provides provable access to the instruction set; the systemhaving a distributed ledger that tokenizes an instruction set for a foodpreparation process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for a polymerproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes a trade secret with an expert wrapper,such that operation on the distributed ledger provides provable accessto the trade secret and the wrapper provides validation of the tradesecret by the expert; the system having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret; the system having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger; the system havinga distributed ledger that tokenizes an item of intellectual property anda reporting system that reports an analytic result based on theoperations performed on the distributed ledger or the intellectualproperty; the system having a distributed ledger that aggregates a setof instructions, where an operation on the distributed ledger adds atleast one instruction to a pre-existing set of instructions to provide amodified set of instructions; the system having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets; thesystem having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location; the system having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment; thesystem having an expert system that uses machine learning to optimizethe execution of cryptocurrency transactions based on tax status; thesystem having an expert system that aggregates regulatory informationcovering cryptocurrency transactions and automatically selects ajurisdiction for an operation based on the regulatory information; thesystem having an expert system that uses machine learning to optimizethe execution of a cryptocurrency transaction based on real time energyprice information for an available energy source; the system having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction; thesystem having an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction; the systemhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction; the system having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a cryptocurrency transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction; the system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction; the system having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources; thesystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected fromautomated agent behavioral data sources; the system having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from automated agent behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from automated agent behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from business entitybehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources; thesystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from businessentity behavioral data sources; the system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources;the system having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from humanbehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from human behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from human behavioral data sources; the system having anexpert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction; the system having an intelligent agent thatis configured to solicit the attention resources of another externalintelligent agent; the system having a machine that automaticallypurchases attention resources in a forward market for attention; thesystem having a fleet of machines that automatically aggregatepurchasing in a forward market for attention; the system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome; the system having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a facility production outcome; the systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles;the system having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizeprovisioning and allocation of energy and compute resources to produce afavorable facility resource output selection among a set of availableoutputs; the system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles; the system having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles; the system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations; the system having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility; the system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility; the system having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources; thesystem having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources; the systemhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter; the system having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility;and/or the system having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of parameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes serverless code logic, such thatoperation on the distributed ledger provides provable access to theserverless code logic. Certain further aspects of the exampletransaction-enabling system are described following, any one or more ofwhich may be present in certain embodiments: the system having adistributed ledger that tokenizes an instruction set for a crystalfabrication system, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for a foodpreparation process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for a polymerproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes a trade secret with an expert wrapper,such that operation on the distributed ledger provides provable accessto the trade secret and the wrapper provides validation of the tradesecret by the expert; the system having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret; the system having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger; the system havinga distributed ledger that tokenizes an item of intellectual property anda reporting system that reports an analytic result based on theoperations performed on the distributed ledger or the intellectualproperty; the system having a distributed ledger that aggregates a setof instructions, where an operation on the distributed ledger adds atleast one instruction to a pre-existing set of instructions to provide amodified set of instructions; the system having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets; thesystem having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location; the system having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment; thesystem having an expert system that uses machine learning to optimizethe execution of cryptocurrency transactions based on tax status; thesystem having an expert system that aggregates regulatory informationcovering cryptocurrency transactions and automatically selects ajurisdiction for an operation based on the regulatory information; thesystem having an expert system that uses machine learning to optimizethe execution of a cryptocurrency transaction based on real time energyprice information for an available energy source; the system having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction; thesystem having an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction; the systemhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction; the system having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a cryptocurrency transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction; the system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction; the system having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources; thesystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected fromautomated agent behavioral data sources; the system having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from automated agent behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from automated agent behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from business entitybehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources; thesystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from businessentity behavioral data sources; the system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources;the system having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from humanbehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from human behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from human behavioral data sources; the system having anexpert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction; the system having an intelligent agent thatis configured to solicit the attention resources of another externalintelligent agent; the system having a machine that automaticallypurchases attention resources in a forward market for attention; thesystem having a fleet of machines that automatically aggregatepurchasing in a forward market for attention; the system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome; the system having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a facility production outcome; the systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles;the system having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizeprovisioning and allocation of energy and compute resources to produce afavorable facility resource output selection among a set of availableoutputs; the system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles; the system having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles; the system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations; the system having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility; the system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility; the system having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources; thesystem having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources; the systemhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter; the system having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility; thesystem having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofparameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes an instruction set for a crystalfabrication system, such that operation on the distributed ledgerprovides provable access to the instruction set. Certain further aspectsof the example transaction-enabling system are described following, anyone or more of which may be present in certain embodiments: the systemhaving a distributed ledger that tokenizes an instruction set for a foodpreparation process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for a polymerproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set; the system having adistributed ledger that tokenizes a trade secret with an expert wrapper,such that operation on the distributed ledger provides provable accessto the trade secret and the wrapper provides validation of the tradesecret by the expert; the system having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret; the system having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction; execution of the instruction set on a system results inrecording a transaction in the distributed ledger; the system having adistributed ledger that tokenizes an item of intellectual property and areporting system that reports an analytic result based on the operationsperformed on the distributed ledger or the intellectual property; thesystem having a distributed ledger that aggregates a set ofinstructions, where an operation on the distributed ledger adds at leastone instruction to a pre-existing set of instructions to provide amodified set of instructions; the system having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets; thesystem having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location; the system having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment; thesystem having an expert system that uses machine learning to optimizethe execution of cryptocurrency transactions based on tax status; thesystem having an expert system that aggregates regulatory informationcovering cryptocurrency transactions and automatically selects ajurisdiction for an operation based on the regulatory information; thesystem having an expert system that uses machine learning to optimizethe execution of a cryptocurrency transaction based on real time energyprice information for an available energy source; the system having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction; thesystem having an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction; the systemhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction; the system having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a cryptocurrency transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction; the system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction; the system having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources; thesystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected fromautomated agent behavioral data sources; the system having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from automated agent behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from automated agent behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from business entitybehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources; thesystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from businessentity behavioral data sources; the system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources;the system having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from humanbehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from human behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from human behavioral data sources; the system having anexpert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction; the system having an intelligent agent thatis configured to solicit the attention resources of another externalintelligent agent; the system having a machine that automaticallypurchases attention resources in a forward market for attention; thesystem having a fleet of machines that automatically aggregatepurchasing in a forward market for attention; the system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome; the system having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a facility production outcome; the systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles;the system having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizeprovisioning and allocation of energy and compute resources to produce afavorable facility resource output selection among a set of availableoutputs; the system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles; the system having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles; the system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations; the system having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility; the system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility; the system having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources; thesystem having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources; the systemhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter; the system having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility; thesystem having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofparameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes an instruction set for a foodpreparation process, such that operation on the distributed ledgerprovides provable access to the instruction set. Certain further aspectsof the example transaction-enabling system are described following, anyone or more of which may be present in certain embodiments: the systemhaving a distributed ledger that tokenizes an instruction set for apolymer production process, such that operation on the distributedledger provides provable access to the instruction set; the systemhaving a distributed ledger that tokenizes an instruction set forchemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set; the systemhaving a distributed ledger that tokenizes an instruction set for abiological production process, such that operation on the distributedledger provides provable access to the instruction set; the systemhaving a distributed ledger that tokenizes a trade secret with an expertwrapper, such that operation on the distributed ledger provides provableaccess to the trade secret and the wrapper provides validation of thetrade secret by the expert; the system having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret; the system having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger; the system havinga distributed ledger that tokenizes an item of intellectual property anda reporting system that reports an analytic result based on theoperations performed on the distributed ledger or the intellectualproperty; the system having a distributed ledger that aggregates a setof instructions, where an operation on the distributed ledger adds atleast one instruction to a pre-existing set of instructions to provide amodified set of instructions; the system having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets; thesystem having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location; the system having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment; thesystem having an expert system that uses machine learning to optimizethe execution of cryptocurrency transactions based on tax status; thesystem having an expert system that aggregates regulatory informationcovering cryptocurrency transactions and automatically selects ajurisdiction for an operation based on the regulatory information; thesystem having an expert system that uses machine learning to optimizethe execution of a cryptocurrency transaction based on real time energyprice information for an available energy source; the system having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction; thesystem having an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction; the systemhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction; the system having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a cryptocurrency transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction; the system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction; the system having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction; thesystem having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction; the system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources; thesystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected fromautomated agent behavioral data sources; the system having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from automated agent behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from automated agent behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from business entitybehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources; thesystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from businessentity behavioral data sources; the system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources;the system having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from humanbehavioral data sources; the system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources; the systemhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from human behavioral datasources; the system having a machine that automatically forecastsforward market value of compute capability based on informationcollected from human behavioral data sources; the system having anexpert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction; the system having an intelligent agent thatis configured to solicit the attention resources of another externalintelligent agent; the system having a machine that automaticallypurchases attention resources in a forward market for attention; thesystem having a fleet of machines that automatically aggregatepurchasing in a forward market for attention; the system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome; the system having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a facility production outcome; the systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles;the system having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizeprovisioning and allocation of energy and compute resources to produce afavorable facility resource output selection among a set of availableoutputs; the system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles; the system having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles; the system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations; the system having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility; the system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility; the system having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources; thesystem having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources; the systemhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter; the system having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility; thesystem having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofparameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes an instruction set for a polymerproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set for a polymer productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set and having a distributed ledger thattokenizes an instruction set for chemical synthesis process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a polymer production process, such that operationon the distributed ledger provides provable access to the instructionset and having a distributed ledger that tokenizes an instruction setfor a biological production process, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for a polymerproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set and having a distributedledger that tokenizes a trade secret with an expert wrapper, such thatoperation on the distributed ledger provides provable access to thetrade secret and the wrapper provides validation of the trade secret bythe expert. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set fora polymer production process, such that operation on the distributedledger provides provable access to the instruction set and having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for a polymerproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set and having a distributedledger that tokenizes an instruction set, such that operation on thedistributed ledger provides provable access to the instruction set andexecution of the instruction set on a system results in recording atransaction in the distributed ledger. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thattokenizes an instruction set for a polymer production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a distributed ledger that tokenizes an itemof intellectual property and a reporting system that reports an analyticresult based on the operations performed on the distributed ledger orthe intellectual property. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a polymer production process, such that operationon the distributed ledger provides provable access to the instructionset and having a distributed ledger that aggregates a set ofinstructions, where an operation on the distributed ledger adds at leastone instruction to a pre-existing set of instructions to provide amodified set of instructions. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a polymer production process, such that operationon the distributed ledger provides provable access to the instructionset and having a smart wrapper for management of a distributed ledgerthat aggregates sets of instructions, where the smart wrapper managesallocation of instruction sub-sets to the distributed ledger and accessto the instruction sub-sets. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a polymer production process, such that operationon the distributed ledger provides provable access to the instructionset and having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a polymer production process, such that operationon the distributed ledger provides provable access to the instructionset and having a self-executing cryptocurrency coin that commits atransaction upon recognizing a location-based parameter that providesfavorable tax treatment. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a polymer production process, such that operationon the distributed ledger provides provable access to the instructionset and having an expert system that uses machine learning to optimizethe execution of cryptocurrency transactions based on tax status. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for a polymerproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set and having an expertsystem that aggregates regulatory information covering cryptocurrencytransactions and automatically selects a jurisdiction for an operationbased on the regulatory information. In embodiments, provided herein isa transaction-enabling system having a distributed ledger that tokenizesan instruction set for a polymer production process, such that operationon the distributed ledger provides provable access to the instructionset and having an expert system that uses machine learning to optimizethe execution of a cryptocurrency transaction based on real time energyprice information for an available energy source. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set for a polymer productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set and having an expert system that usesmachine learning to optimize the execution of a cryptocurrencytransaction based on an understanding of available energy sources topower computing resources to execute the transaction. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set for a polymer productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set and having an expert system that usesmachine learning to optimize charging and recharging cycle of arechargeable battery system to provide energy for execution of acryptocurrency transaction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a polymer production process, such that operationon the distributed ledger provides provable access to the instructionset and having an expert system that predicts a forward market price ina market based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a polymer production process, such that operationon the distributed ledger provides provable access to the instructionset and having an expert system that predicts a forward market price ina market based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set fora polymer production process, such that operation on the distributedledger provides provable access to the instruction set and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set fora polymer production process, such that operation on the distributedledger provides provable access to the instruction set and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set fora polymer production process, such that operation on the distributedledger provides provable access to the instruction set and having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set fora polymer production process, such that operation on the distributedledger provides provable access to the instruction set and having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set fora polymer production process, such that operation on the distributedledger provides provable access to the instruction set and having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a polymer production process, such that operationon the distributed ledger provides provable access to the instructionset and having an expert system that predicts a forward market price ina market for spectrum or network bandwidth based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set for a polymer productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set and having an expert system that predictsa forward market price in a market for computing resources based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for a polymerproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set and having an expertsystem that predicts a forward market price in a market for advertisingbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set fora polymer production process, such that operation on the distributedledger provides provable access to the instruction set and having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a polymer production process, such that operationon the distributed ledger provides provable access to the instructionset and having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from automatedagent behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a polymer production process, such that operationon the distributed ledger provides provable access to the instructionset and having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thattokenizes an instruction set for a polymer production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom automated agent behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes an instruction set for a polymer production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set for a polymer productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set for a polymer productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for a polymerproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set and having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from business entity behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set fora polymer production process, such that operation on the distributedledger provides provable access to the instruction set and having amachine that automatically forecasts forward market value of computecapability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a polymer production process, such that operationon the distributed ledger provides provable access to the instructionset and having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from humanbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a polymer production process, such that operationon the distributed ledger provides provable access to the instructionset and having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from humanbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a polymer production process, such that operationon the distributed ledger provides provable access to the instructionset and having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from humanbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a polymer production process, such that operationon the distributed ledger provides provable access to the instructionset and having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from humanbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a polymer production process, such that operationon the distributed ledger provides provable access to the instructionset and having an expert system that predicts a forward market price ina market for spectrum or network bandwidth based on an understandingobtained by analyzing social data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thattokenizes an instruction set for a polymer production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having an intelligent agent that is configured tosolicit the attention resources of another external intelligent agent.In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes an instruction set for a polymerproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set and having a machinethat automatically purchases attention resources in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set fora polymer production process, such that operation on the distributedledger provides provable access to the instruction set and having afleet of machines that automatically aggregate purchasing in a forwardmarket for attention. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a polymer production process, such that operationon the distributed ledger provides provable access to the instructionset and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to predict alikelihood of a facility production outcome. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes an instruction set for a polymer production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a facility production outcome. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thattokenizes an instruction set for a polymer production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a polymer production process, such that operationon the distributed ledger provides provable access to the instructionset and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizeprovisioning and allocation of energy and compute resources to produce afavorable facility resource output selection among a set of availableoutputs. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set fora polymer production process, such that operation on the distributedledger provides provable access to the instruction set and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize requisition andprovisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set fora polymer production process, such that operation on the distributedledger provides provable access to the instruction set and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for a polymerproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thattokenizes an instruction set for a polymer production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for a polymerproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set fora polymer production process, such that operation on the distributedledger provides provable access to the instruction set and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of input resources. In embodiments, provided herein isa transaction-enabling system having a distributed ledger that tokenizesan instruction set for a polymer production process, such that operationon the distributed ledger provides provable access to the instructionset and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for a polymerproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a polymer production process, such that operationon the distributed ledger provides provable access to the instructionset and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a utilization parameter for the outputof the facility. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a polymer production process, such that operationon the distributed ledger provides provable access to the instructionset and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofparameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set for chemical synthesis process,such that operation on the distributed ledger provides provable accessto the instruction set and having a distributed ledger that tokenizes aninstruction set for a biological production process, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a distributed ledger that tokenizes an instruction set forchemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set and having adistributed ledger that tokenizes a trade secret with an expert wrapper,such that operation on the distributed ledger provides provable accessto the trade secret and the wrapper provides validation of the tradesecret by the expert. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for chemical synthesis process, such that operationon the distributed ledger provides provable access to the instructionset and having a distributed ledger that aggregates views of a tradesecret into a chain that proves which and how many parties have viewedthe trade secret. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for chemical synthesis process, such that operationon the distributed ledger provides provable access to the instructionset and having a distributed ledger that tokenizes an instruction set,such that operation on the distributed ledger provides provable accessto the instruction set and execution of the instruction set on a systemresults in recording a transaction in the distributed ledger. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set and having a distributedledger that tokenizes an item of intellectual property and a reportingsystem that reports an analytic result based on the operations performedon the distributed ledger or the intellectual property. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set for chemical synthesis process,such that operation on the distributed ledger provides provable accessto the instruction set and having a distributed ledger that aggregates aset of instructions, where an operation on the distributed ledger addsat least one instruction to a pre-existing set of instructions toprovide a modified set of instructions. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thattokenizes an instruction set for chemical synthesis process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a smart wrapper for management of adistributed ledger that aggregates sets of instructions, where the smartwrapper manages allocation of instruction sub-sets to the distributedledger and access to the instruction sub-sets. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes an instruction set for chemical synthesis process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a smart wrapper for a cryptocurrency cointhat directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes an instruction set for chemical synthesis process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a self-executing cryptocurrency coin thatcommits a transaction upon recognizing a location-based parameter thatprovides favorable tax treatment. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for chemical synthesis process, such that operationon the distributed ledger provides provable access to the instructionset and having an expert system that uses machine learning to optimizethe execution of cryptocurrency transactions based on tax status. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set and having an expertsystem that aggregates regulatory information covering cryptocurrencytransactions and automatically selects a jurisdiction for an operationbased on the regulatory information. In embodiments, provided herein isa transaction-enabling system having a distributed ledger that tokenizesan instruction set for chemical synthesis process, such that operationon the distributed ledger provides provable access to the instructionset and having an expert system that uses machine learning to optimizethe execution of a cryptocurrency transaction based on real time energyprice information for an available energy source. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set for chemical synthesis process,such that operation on the distributed ledger provides provable accessto the instruction set and having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon an understanding of available energy sources to power computingresources to execute the transaction. In embodiments, provided herein isa transaction-enabling system having a distributed ledger that tokenizesan instruction set for chemical synthesis process, such that operationon the distributed ledger provides provable access to the instructionset and having an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set and having an expertsystem that predicts a forward market price in a market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set and having an expertsystem that predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set and having an expertsystem that predicts a forward market price in a market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set forchemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set forchemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set and having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set forchemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set and having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set forchemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set and having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for chemical synthesis process, such that operationon the distributed ledger provides provable access to the instructionset and having an expert system that predicts a forward market price ina market for spectrum or network bandwidth based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set for chemical synthesis process,such that operation on the distributed ledger provides provable accessto the instruction set and having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set and having an expertsystem that predicts a forward market price in a market for advertisingbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set forchemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set and having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for chemical synthesis process, such that operationon the distributed ledger provides provable access to the instructionset and having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from automatedagent behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for chemical synthesis process, such that operationon the distributed ledger provides provable access to the instructionset and having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thattokenizes an instruction set for chemical synthesis process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom automated agent behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes an instruction set for chemical synthesis process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set for chemical synthesis process,such that operation on the distributed ledger provides provable accessto the instruction set and having a machine that automatically forecastsforward market pricing of energy prices based on information collectedfrom business entity behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes an instruction set for chemical synthesis process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set for chemical synthesis process,such that operation on the distributed ledger provides provable accessto the instruction set and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom business entity behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes an instruction set for chemical synthesis process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set for chemical synthesis process,such that operation on the distributed ledger provides provable accessto the instruction set and having a machine that automatically forecastsforward market pricing of energy prices based on information collectedfrom human behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for chemical synthesis process, such that operationon the distributed ledger provides provable access to the instructionset and having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from humanbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for chemical synthesis process, such that operationon the distributed ledger provides provable access to the instructionset and having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from humanbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for chemical synthesis process, such that operationon the distributed ledger provides provable access to the instructionset and having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from humanbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for chemical synthesis process, such that operationon the distributed ledger provides provable access to the instructionset and having an expert system that predicts a forward market price ina market for spectrum or network bandwidth based on an understandingobtained by analyzing social data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thattokenizes an instruction set for chemical synthesis process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having an intelligent agent that is configured tosolicit the attention resources of another external intelligent agent.In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set and having a machinethat automatically purchases attention resources in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set forchemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set and having afleet of machines that automatically aggregate purchasing in a forwardmarket for attention. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for chemical synthesis process, such that operationon the distributed ledger provides provable access to the instructionset and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to predict alikelihood of a facility production outcome. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes an instruction set for chemical synthesis process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a facility production outcome. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thattokenizes an instruction set for chemical synthesis process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for chemical synthesis process, such that operationon the distributed ledger provides provable access to the instructionset and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizeprovisioning and allocation of energy and compute resources to produce afavorable facility resource output selection among a set of availableoutputs. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set forchemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize requisition andprovisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set forchemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thattokenizes an instruction set for chemical synthesis process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set forchemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of input resources. In embodiments, provided herein isa transaction-enabling system having a distributed ledger that tokenizesan instruction set for chemical synthesis process, such that operationon the distributed ledger provides provable access to the instructionset and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for chemical synthesis process, such that operationon the distributed ledger provides provable access to the instructionset and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a utilization parameter for the outputof the facility. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for chemical synthesis process, such that operationon the distributed ledger provides provable access to the instructionset and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofparameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set for a biological productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set and having a distributed ledger thattokenizes a trade secret with an expert wrapper, such that operation onthe distributed ledger provides provable access to the trade secret andthe wrapper provides validation of the trade secret by the expert. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set and having a distributedledger that aggregates views of a trade secret into a chain that proveswhich and how many parties have viewed the trade secret. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set for a biological productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set and having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set and execution ofthe instruction set on a system results in recording a transaction inthe distributed ledger. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a biological production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a distributed ledger that tokenizes an itemof intellectual property and a reporting system that reports an analyticresult based on the operations performed on the distributed ledger orthe intellectual property. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a biological production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a distributed ledger that aggregates a set ofinstructions, where an operation on the distributed ledger adds at leastone instruction to a pre-existing set of instructions to provide amodified set of instructions. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a biological production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a smart wrapper for management of adistributed ledger that aggregates sets of instructions, where the smartwrapper manages allocation of instruction sub-sets to the distributedledger and access to the instruction sub-sets. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes an instruction set for a biological production process, suchthat operation on the distributed ledger provides provable access to theinstruction set and having a smart wrapper for a cryptocurrency cointhat directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes an instruction set for a biological production process, suchthat operation on the distributed ledger provides provable access to theinstruction set and having a self-executing cryptocurrency coin thatcommits a transaction upon recognizing a location-based parameter thatprovides favorable tax treatment. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a biological production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having an expert system that uses machine learningto optimize the execution of cryptocurrency transactions based on taxstatus. In embodiments, provided herein is a transaction-enabling systemhaving a distributed ledger that tokenizes an instruction set for abiological production process, such that operation on the distributedledger provides provable access to the instruction set and having anexpert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set for a biological productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set and having an expert system that usesmachine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set fora biological production process, such that operation on the distributedledger provides provable access to the instruction set and having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set and having an expertsystem that uses machine learning to optimize charging and rechargingcycle of a rechargeable battery system to provide energy for executionof a cryptocurrency transaction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a biological production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a biological production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a biological production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set for a biological productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set and having an expert system that predictsa forward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set for a biological productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set and having an expert system that predictsa forward market price in an energy market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set for a biological productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set and having an expert system that predictsa forward market price in an energy market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set for a biological productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set and having an expert system that predictsa forward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set and having an expertsystem that predicts a forward market price in a market for spectrum ornetwork bandwidth based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a biological production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having an expert system that predicts a forwardmarket price in a market for computing resources based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set and having an expertsystem that predicts a forward market price in a market for advertisingbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set fora biological production process, such that operation on the distributedledger provides provable access to the instruction set and having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a biological production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a machine that automatically forecastsforward market pricing of energy prices based on information collectedfrom automated agent behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes an instruction set for a biological production process, suchthat operation on the distributed ledger provides provable access to theinstruction set and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set for a biological productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set for a biological productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set and having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set and having a machinethat automatically forecasts forward market pricing of energy pricesbased on information collected from business entity behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set fora biological production process, such that operation on the distributedledger provides provable access to the instruction set and having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set fora biological production process, such that operation on the distributedledger provides provable access to the instruction set and having amachine that automatically forecasts forward market pricing of energycredits based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set fora biological production process, such that operation on the distributedledger provides provable access to the instruction set and having amachine that automatically forecasts forward market value of computecapability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a biological production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a machine that automatically forecastsforward market pricing of energy prices based on information collectedfrom human behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a biological production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes an instruction set for a biological production process, suchthat operation on the distributed ledger provides provable access to theinstruction set and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom human behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a biological production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes an instruction set for a biological production process, suchthat operation on the distributed ledger provides provable access to theinstruction set and having an expert system that predicts a forwardmarket price in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing social data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set for a biological productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set and having an intelligent agent that isconfigured to solicit the attention resources of another externalintelligent agent. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a biological production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a machine that automatically purchasesattention resources in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set for a biological productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set and having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a biological production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a facility production outcome. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thattokenizes an instruction set for a biological production process, suchthat operation on the distributed ledger provides provable access to theinstruction set and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a biological production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a biological production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a biological production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a biological production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set for a biological production process, such thatoperation on the distributed ledger provides provable access to theinstruction set and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set for a biological productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of inputresources. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set fora biological production process, such that operation on the distributedledger provides provable access to the instruction set and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of facility resources. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thattokenizes an instruction set for a biological production process, suchthat operation on the distributed ledger provides provable access to theinstruction set and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to an output parameter. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes a trade secret with an expertwrapper, such that operation on the distributed ledger provides provableaccess to the trade secret and the wrapper provides validation of thetrade secret by the expert. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes atrade secret with an expert wrapper, such that operation on thedistributed ledger provides provable access to the trade secret and thewrapper provides validation of the trade secret by the expert and havinga distributed ledger that aggregates views of a trade secret into achain that proves which and how many parties have viewed the tradesecret. In embodiments, provided herein is a transaction-enabling systemhaving a distributed ledger that tokenizes a trade secret with an expertwrapper, such that operation on the distributed ledger provides provableaccess to the trade secret and the wrapper provides validation of thetrade secret by the expert and having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set and execution ofthe instruction set on a system results in recording a transaction inthe distributed ledger. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes atrade secret with an expert wrapper, such that operation on thedistributed ledger provides provable access to the trade secret and thewrapper provides validation of the trade secret by the expert and havinga distributed ledger that tokenizes an item of intellectual property anda reporting system that reports an analytic result based on theoperations performed on the distributed ledger or the intellectualproperty. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes a trade secret with anexpert wrapper, such that operation on the distributed ledger providesprovable access to the trade secret and the wrapper provides validationof the trade secret by the expert and having a distributed ledger thataggregates a set of instructions, where an operation on the distributedledger adds at least one instruction to a pre-existing set ofinstructions to provide a modified set of instructions. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a trade secret with an expert wrapper, such thatoperation on the distributed ledger provides provable access to thetrade secret and the wrapper provides validation of the trade secret bythe expert and having a smart wrapper for management of a distributedledger that aggregates sets of instructions, where the smart wrappermanages allocation of instruction sub-sets to the distributed ledger andaccess to the instruction sub-sets. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes atrade secret with an expert wrapper, such that operation on thedistributed ledger provides provable access to the trade secret and thewrapper provides validation of the trade secret by the expert and havinga smart wrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes atrade secret with an expert wrapper, such that operation on thedistributed ledger provides provable access to the trade secret and thewrapper provides validation of the trade secret by the expert and havinga self-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes a trade secret with anexpert wrapper, such that operation on the distributed ledger providesprovable access to the trade secret and the wrapper provides validationof the trade secret by the expert and having an expert system that usesmachine learning to optimize the execution of cryptocurrencytransactions based on tax status. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes atrade secret with an expert wrapper, such that operation on thedistributed ledger provides provable access to the trade secret and thewrapper provides validation of the trade secret by the expert and havingan expert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a trade secret with an expert wrapper, such thatoperation on the distributed ledger provides provable access to thetrade secret and the wrapper provides validation of the trade secret bythe expert and having an expert system that uses machine learning tooptimize the execution of a cryptocurrency transaction based on realtime energy price information for an available energy source. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes a trade secret with an expert wrapper,such that operation on the distributed ledger provides provable accessto the trade secret and the wrapper provides validation of the tradesecret by the expert and having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon an understanding of available energy sources to power computingresources to execute the transaction. In embodiments, provided herein isa transaction-enabling system having a distributed ledger that tokenizesa trade secret with an expert wrapper, such that operation on thedistributed ledger provides provable access to the trade secret and thewrapper provides validation of the trade secret by the expert and havingan expert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes a trade secret with an expert wrapper, such that operation onthe distributed ledger provides provable access to the trade secret andthe wrapper provides validation of the trade secret by the expert andhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes a trade secret with anexpert wrapper, such that operation on the distributed ledger providesprovable access to the trade secret and the wrapper provides validationof the trade secret by the expert and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a trade secret with an expert wrapper, such thatoperation on the distributed ledger provides provable access to thetrade secret and the wrapper provides validation of the trade secret bythe expert and having an expert system that predicts a forward marketprice in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a trade secret with an expert wrapper, such thatoperation on the distributed ledger provides provable access to thetrade secret and the wrapper provides validation of the trade secret bythe expert and having an expert system that predicts a forward marketprice in a market based on an understanding obtained by analyzing socialnetwork data sources and executes a cryptocurrency transaction based onthe forward market prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes atrade secret with an expert wrapper, such that operation on thedistributed ledger provides provable access to the trade secret and thewrapper provides validation of the trade secret by the expert and havingan expert system that predicts a forward market price in an energymarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes atrade secret with an expert wrapper, such that operation on thedistributed ledger provides provable access to the trade secret and thewrapper provides validation of the trade secret by the expert and havingan expert system that predicts a forward market price in an energymarket based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes a trade secret with anexpert wrapper, such that operation on the distributed ledger providesprovable access to the trade secret and the wrapper provides validationof the trade secret by the expert and having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes a trade secret with anexpert wrapper, such that operation on the distributed ledger providesprovable access to the trade secret and the wrapper provides validationof the trade secret by the expert and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes atrade secret with an expert wrapper, such that operation on thedistributed ledger provides provable access to the trade secret and thewrapper provides validation of the trade secret by the expert and havingan expert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes atrade secret with an expert wrapper, such that operation on thedistributed ledger provides provable access to the trade secret and thewrapper provides validation of the trade secret by the expert and havingan expert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes atrade secret with an expert wrapper, such that operation on thedistributed ledger provides provable access to the trade secret and thewrapper provides validation of the trade secret by the expert and havingan expert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes atrade secret with an expert wrapper, such that operation on thedistributed ledger provides provable access to the trade secret and thewrapper provides validation of the trade secret by the expert and havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes a trade secret with anexpert wrapper, such that operation on the distributed ledger providesprovable access to the trade secret and the wrapper provides validationof the trade secret by the expert and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes a trade secret with an expertwrapper, such that operation on the distributed ledger provides provableaccess to the trade secret and the wrapper provides validation of thetrade secret by the expert and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a trade secret with an expert wrapper, such thatoperation on the distributed ledger provides provable access to thetrade secret and the wrapper provides validation of the trade secret bythe expert and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thattokenizes a trade secret with an expert wrapper, such that operation onthe distributed ledger provides provable access to the trade secret andthe wrapper provides validation of the trade secret by the expert andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes atrade secret with an expert wrapper, such that operation on thedistributed ledger provides provable access to the trade secret and thewrapper provides validation of the trade secret by the expert and havinga machine that automatically forecasts forward market pricing of networkspectrum based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes a trade secret with anexpert wrapper, such that operation on the distributed ledger providesprovable access to the trade secret and the wrapper provides validationof the trade secret by the expert and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes a trade secret with an expertwrapper, such that operation on the distributed ledger provides provableaccess to the trade secret and the wrapper provides validation of thetrade secret by the expert and having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes a trade secret with an expert wrapper,such that operation on the distributed ledger provides provable accessto the trade secret and the wrapper provides validation of the tradesecret by the expert and having a machine that automatically forecastsforward market pricing of energy prices based on information collectedfrom human behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes atrade secret with an expert wrapper, such that operation on thedistributed ledger provides provable access to the trade secret and thewrapper provides validation of the trade secret by the expert and havinga machine that automatically forecasts forward market pricing of networkspectrum based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes a trade secret with anexpert wrapper, such that operation on the distributed ledger providesprovable access to the trade secret and the wrapper provides validationof the trade secret by the expert and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes a trade secret with an expert wrapper,such that operation on the distributed ledger provides provable accessto the trade secret and the wrapper provides validation of the tradesecret by the expert and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes a trade secret with an expert wrapper, such that operation onthe distributed ledger provides provable access to the trade secret andthe wrapper provides validation of the trade secret by the expert andhaving an expert system that predicts a forward market price in a marketfor spectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes atrade secret with an expert wrapper, such that operation on thedistributed ledger provides provable access to the trade secret and thewrapper provides validation of the trade secret by the expert and havingan intelligent agent that is configured to solicit the attentionresources of another external intelligent agent. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a trade secret with an expert wrapper, such thatoperation on the distributed ledger provides provable access to thetrade secret and the wrapper provides validation of the trade secret bythe expert and having a machine that automatically purchases attentionresources in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes a trade secret with an expert wrapper, such that operation onthe distributed ledger provides provable access to the trade secret andthe wrapper provides validation of the trade secret by the expert andhaving a fleet of machines that automatically aggregate purchasing in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes atrade secret with an expert wrapper, such that operation on thedistributed ledger provides provable access to the trade secret and thewrapper provides validation of the trade secret by the expert and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes atrade secret with an expert wrapper, such that operation on thedistributed ledger provides provable access to the trade secret and thewrapper provides validation of the trade secret by the expert and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes a trade secret with anexpert wrapper, such that operation on the distributed ledger providesprovable access to the trade secret and the wrapper provides validationof the trade secret by the expert and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource utilizationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes a trade secret with an expert wrapper, such that operation onthe distributed ledger provides provable access to the trade secret andthe wrapper provides validation of the trade secret by the expert andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes a trade secret with an expert wrapper,such that operation on the distributed ledger provides provable accessto the trade secret and the wrapper provides validation of the tradesecret by the expert and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes atrade secret with an expert wrapper, such that operation on thedistributed ledger provides provable access to the trade secret and thewrapper provides validation of the trade secret by the expert and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes a trade secret with an expert wrapper,such that operation on the distributed ledger provides provable accessto the trade secret and the wrapper provides validation of the tradesecret by the expert and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes a trade secret with an expert wrapper,such that operation on the distributed ledger provides provable accessto the trade secret and the wrapper provides validation of the tradesecret by the expert and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes a trade secret with an expert wrapper,such that operation on the distributed ledger provides provable accessto the trade secret and the wrapper provides validation of the tradesecret by the expert and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to at least one of aninput resource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes a trade secret with an expert wrapper, such thatoperation on the distributed ledger provides provable access to thetrade secret and the wrapper provides validation of the trade secret bythe expert and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes a trade secret with an expert wrapper,such that operation on the distributed ledger provides provable accessto the trade secret and the wrapper provides validation of the tradesecret by the expert and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes a trade secret with anexpert wrapper, such that operation on the distributed ledger providesprovable access to the trade secret and the wrapper provides validationof the trade secret by the expert and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to anoutput parameter. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizes atrade secret with an expert wrapper, such that operation on thedistributed ledger provides provable access to the trade secret and thewrapper provides validation of the trade secret by the expert and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a utilization parameter for the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes a trade secret with anexpert wrapper, such that operation on the distributed ledger providesprovable access to the trade secret and the wrapper provides validationof the trade secret by the expert and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of parameters received from a digital twinfor the facility.

In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that aggregates views of a trade secret into achain that proves which and how many parties have viewed the tradesecret. In embodiments, provided herein is a transaction-enabling systemhaving a distributed ledger that aggregates views of a trade secret intoa chain that proves which and how many parties have viewed the tradesecret and having a distributed ledger that tokenizes an instructionset, such that operation on the distributed ledger provides provableaccess to the instruction set and execution of the instruction set on asystem results in recording a transaction in the distributed ledger. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret andhaving a distributed ledger that tokenizes an item of intellectualproperty and a reporting system that reports an analytic result based onthe operations performed on the distributed ledger or the intellectualproperty. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that aggregates views of a tradesecret into a chain that proves which and how many parties have viewedthe trade secret and having a distributed ledger that aggregates a setof instructions, where an operation on the distributed ledger adds atleast one instruction to a pre-existing set of instructions to provide amodified set of instructions. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesviews of a trade secret into a chain that proves which and how manyparties have viewed the trade secret and having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret andhaving a smart wrapper for a cryptocurrency coin that directs executionof a transaction involving the coin to a geographic location based ontax treatment of at least one of the coin and the transaction in thegeographic location. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesviews of a trade secret into a chain that proves which and how manyparties have viewed the trade secret and having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret andhaving an expert system that uses machine learning to optimize theexecution of cryptocurrency transactions based on tax status. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret andhaving an expert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information. In embodiments,provided herein is a transaction-enabling system having a distributedledger that aggregates views of a trade secret into a chain that proveswhich and how many parties have viewed the trade secret and having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on real time energy price informationfor an available energy source. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesviews of a trade secret into a chain that proves which and how manyparties have viewed the trade secret and having an expert system thatuses machine learning to optimize the execution of a cryptocurrencytransaction based on an understanding of available energy sources topower computing resources to execute the transaction. In embodiments,provided herein is a transaction-enabling system having a distributedledger that aggregates views of a trade secret into a chain that proveswhich and how many parties have viewed the trade secret and having anexpert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret and having an expertsystem that predicts a forward market price in a market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret andhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that aggregates views of a tradesecret into a chain that proves which and how many parties have viewedthe trade secret and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a distributedledger that aggregates views of a trade secret into a chain that proveswhich and how many parties have viewed the trade secret and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that aggregates views of a tradesecret into a chain that proves which and how many parties have viewedthe trade secret and having an expert system that predicts a forwardmarket price in an energy market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret and having an expertsystem that predicts a forward market price in an energy market based onan understanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret andhaving an expert system that predicts a forward market price in a marketfor computing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesviews of a trade secret into a chain that proves which and how manyparties have viewed the trade secret and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesviews of a trade secret into a chain that proves which and how manyparties have viewed the trade secret and having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that aggregates views of a tradesecret into a chain that proves which and how many parties have viewedthe trade secret and having an expert system that predicts a forwardmarket price in a market for advertising based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a distributedledger that aggregates views of a trade secret into a chain that proveswhich and how many parties have viewed the trade secret and having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesviews of a trade secret into a chain that proves which and how manyparties have viewed the trade secret and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesviews of a trade secret into a chain that proves which and how manyparties have viewed the trade secret and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that aggregates views of a trade secret into achain that proves which and how many parties have viewed the tradesecret and having a machine that automatically forecasts forward marketvalue of compute capability based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesviews of a trade secret into a chain that proves which and how manyparties have viewed the trade secret and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that aggregates views of a trade secret into achain that proves which and how many parties have viewed the tradesecret and having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from businessentity behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesviews of a trade secret into a chain that proves which and how manyparties have viewed the trade secret and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that aggregates views of a tradesecret into a chain that proves which and how many parties have viewedthe trade secret and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom human behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesviews of a trade secret into a chain that proves which and how manyparties have viewed the trade secret and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret andhaving an expert system that predicts a forward market price in a marketfor spectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesviews of a trade secret into a chain that proves which and how manyparties have viewed the trade secret and having an intelligent agentthat is configured to solicit the attention resources of anotherexternal intelligent agent. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesviews of a trade secret into a chain that proves which and how manyparties have viewed the trade secret and having a machine thatautomatically purchases attention resources in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that aggregates views of a tradesecret into a chain that proves which and how many parties have viewedthe trade secret and having a fleet of machines that automaticallyaggregate purchasing in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having a distributedledger that aggregates views of a trade secret into a chain that proveswhich and how many parties have viewed the trade secret and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesviews of a trade secret into a chain that proves which and how manyparties have viewed the trade secret and having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a facility production outcome. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that aggregates views of a trade secret into achain that proves which and how many parties have viewed the tradesecret and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizeprovisioning and allocation of energy and compute resources to produce afavorable facility resource output selection among a set of availableoutputs. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that aggregates views of a tradesecret into a chain that proves which and how many parties have viewedthe trade secret and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesviews of a trade secret into a chain that proves which and how manyparties have viewed the trade secret and having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesviews of a trade secret into a chain that proves which and how manyparties have viewed the trade secret and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to atleast one of an input resource, a facility resource, an output parameterand an external condition related to the output of the facility. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of input resources. In embodiments,provided herein is a transaction-enabling system having a distributedledger that aggregates views of a trade secret into a chain that proveswhich and how many parties have viewed the trade secret and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of facility resources. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesviews of a trade secret into a chain that proves which and how manyparties have viewed the trade secret and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to autilization parameter for the output of the facility. In embodiments,provided herein is a transaction-enabling system having a distributedledger that aggregates views of a trade secret into a chain that proveswhich and how many parties have viewed the trade secret and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set, such that operation on thedistributed ledger provides provable access to the instruction set andexecution of the instruction set on a system results in recording atransaction in the distributed ledger and having a distributed ledgerthat tokenizes an item of intellectual property and a reporting systemthat reports an analytic result based on the operations performed on thedistributed ledger or the intellectual property. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set, such that operation on thedistributed ledger provides provable access to the instruction set andexecution of the instruction set on a system results in recording atransaction in the distributed ledger and having a distributed ledgerthat aggregates a set of instructions, where an operation on thedistributed ledger adds at least one instruction to a pre-existing setof instructions to provide a modified set of instructions. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger and having a smartwrapper for management of a distributed ledger that aggregates sets ofinstructions, where the smart wrapper manages allocation of instructionsub-sets to the distributed ledger and access to the instructionsub-sets. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set,such that operation on the distributed ledger provides provable accessto the instruction set and execution of the instruction set on a systemresults in recording a transaction in the distributed ledger and havinga smart wrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set, such that operation on the distributed ledgerprovides provable access to the instruction set and execution of theinstruction set on a system results in recording a transaction in thedistributed ledger and having a self-executing cryptocurrency coin thatcommits a transaction upon recognizing a location-based parameter thatprovides favorable tax treatment. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set, such that operation on the distributed ledgerprovides provable access to the instruction set and execution of theinstruction set on a system results in recording a transaction in thedistributed ledger and having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set,such that operation on the distributed ledger provides provable accessto the instruction set and execution of the instruction set on a systemresults in recording a transaction in the distributed ledger and havingan expert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set, such that operation on thedistributed ledger provides provable access to the instruction set andexecution of the instruction set on a system results in recording atransaction in the distributed ledger and having an expert system thatuses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set,such that operation on the distributed ledger provides provable accessto the instruction set and execution of the instruction set on a systemresults in recording a transaction in the distributed ledger and havingan expert system that uses machine learning to optimize the execution ofa cryptocurrency transaction based on an understanding of availableenergy sources to power computing resources to execute the transaction.In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger and having anexpert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set and execution ofthe instruction set on a system results in recording a transaction inthe distributed ledger and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set and execution ofthe instruction set on a system results in recording a transaction inthe distributed ledger and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set, such that operation on the distributed ledgerprovides provable access to the instruction set and execution of theinstruction set on a system results in recording a transaction in thedistributed ledger and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set, such that operation on thedistributed ledger provides provable access to the instruction set andexecution of the instruction set on a system results in recording atransaction in the distributed ledger and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing social network data sources and executes acryptocurrency transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger and having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set,such that operation on the distributed ledger provides provable accessto the instruction set and execution of the instruction set on a systemresults in recording a transaction in the distributed ledger and havingan expert system that predicts a forward market price in an energymarket based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set,such that operation on the distributed ledger provides provable accessto the instruction set and execution of the instruction set on a systemresults in recording a transaction in the distributed ledger and havingan expert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set, such that operation on the distributed ledgerprovides provable access to the instruction set and execution of theinstruction set on a system results in recording a transaction in thedistributed ledger and having an expert system that predicts a forwardmarket price in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger and having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set, such that operation on the distributed ledgerprovides provable access to the instruction set and execution of theinstruction set on a system results in recording a transaction in thedistributed ledger and having an expert system that predicts a forwardmarket price in a market for advertising based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set, such that operation on thedistributed ledger provides provable access to the instruction set andexecution of the instruction set on a system results in recording atransaction in the distributed ledger and having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set,such that operation on the distributed ledger provides provable accessto the instruction set and execution of the instruction set on a systemresults in recording a transaction in the distributed ledger and havinga machine that automatically forecasts forward market pricing of networkspectrum based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set,such that operation on the distributed ledger provides provable accessto the instruction set and execution of the instruction set on a systemresults in recording a transaction in the distributed ledger and havinga machine that automatically forecasts forward market pricing of energycredits based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set,such that operation on the distributed ledger provides provable accessto the instruction set and execution of the instruction set on a systemresults in recording a transaction in the distributed ledger and havinga machine that automatically forecasts forward market value of computecapability based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set, such that operation on the distributed ledgerprovides provable access to the instruction set and execution of theinstruction set on a system results in recording a transaction in thedistributed ledger and having a machine that automatically forecastsforward market pricing of energy prices based on information collectedfrom business entity behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set and execution ofthe instruction set on a system results in recording a transaction inthe distributed ledger and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set, such that operation on thedistributed ledger provides provable access to the instruction set andexecution of the instruction set on a system results in recording atransaction in the distributed ledger and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger and having amachine that automatically forecasts forward market value of computecapability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set, such that operation on the distributed ledgerprovides provable access to the instruction set and execution of theinstruction set on a system results in recording a transaction in thedistributed ledger and having a machine that automatically forecastsforward market pricing of energy prices based on information collectedfrom human behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set, such that operation on the distributed ledgerprovides provable access to the instruction set and execution of theinstruction set on a system results in recording a transaction in thedistributed ledger and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set and execution ofthe instruction set on a system results in recording a transaction inthe distributed ledger and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom human behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set, such that operation on the distributed ledgerprovides provable access to the instruction set and execution of theinstruction set on a system results in recording a transaction in thedistributed ledger and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set and execution ofthe instruction set on a system results in recording a transaction inthe distributed ledger and having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing social data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger and having anintelligent agent that is configured to solicit the attention resourcesof another external intelligent agent. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set and execution ofthe instruction set on a system results in recording a transaction inthe distributed ledger and having a machine that automatically purchasesattention resources in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set, such that operation on thedistributed ledger provides provable access to the instruction set andexecution of the instruction set on a system results in recording atransaction in the distributed ledger and having a fleet of machinesthat automatically aggregate purchasing in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set,such that operation on the distributed ledger provides provable accessto the instruction set and execution of the instruction set on a systemresults in recording a transaction in the distributed ledger and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set, such that operation on the distributed ledgerprovides provable access to the instruction set and execution of theinstruction set on a system results in recording a transaction in thedistributed ledger and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a facility production outcome. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set and execution ofthe instruction set on a system results in recording a transaction inthe distributed ledger and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize provisioning and allocation of energy and compute resourcesto produce a favorable facility resource utilization profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set, such that operation on the distributed ledgerprovides provable access to the instruction set and execution of theinstruction set on a system results in recording a transaction in thedistributed ledger and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set, such that operation on the distributed ledgerprovides provable access to the instruction set and execution of theinstruction set on a system results in recording a transaction in thedistributed ledger and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set, such that operation on the distributed ledgerprovides provable access to the instruction set and execution of theinstruction set on a system results in recording a transaction in thedistributed ledger and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set, such that operation on the distributed ledgerprovides provable access to the instruction set and execution of theinstruction set on a system results in recording a transaction in thedistributed ledger and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan instruction set, such that operation on the distributed ledgerprovides provable access to the instruction set and execution of theinstruction set on a system results in recording a transaction in thedistributed ledger and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to at least one of aninput resource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an instruction set, such that operation on thedistributed ledger provides provable access to the instruction set andexecution of the instruction set on a system results in recording atransaction in the distributed ledger and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of input resources. In embodiments, provided herein isa transaction-enabling system having a distributed ledger that tokenizesan instruction set, such that operation on the distributed ledgerprovides provable access to the instruction set and execution of theinstruction set on a system results in recording a transaction in thedistributed ledger and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an instruction set,such that operation on the distributed ledger provides provable accessto the instruction set and execution of the instruction set on a systemresults in recording a transaction in the distributed ledger and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set and execution ofthe instruction set on a system results in recording a transaction inthe distributed ledger and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a utilizationparameter for the output of the facility. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set and execution ofthe instruction set on a system results in recording a transaction inthe distributed ledger and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of parameters received from a digital twin for thefacility.

In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes an item of intellectual property anda reporting system that reports an analytic result based on theoperations performed on the distributed ledger or the intellectualproperty. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an item ofintellectual property and a reporting system that reports an analyticresult based on the operations performed on the distributed ledger orthe intellectual property and having a distributed ledger thataggregates a set of instructions, where an operation on the distributedledger adds at least one instruction to a pre-existing set ofinstructions to provide a modified set of instructions. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an item of intellectual property and a reportingsystem that reports an analytic result based on the operations performedon the distributed ledger or the intellectual property and having asmart wrapper for management of a distributed ledger that aggregatessets of instructions, where the smart wrapper manages allocation ofinstruction sub-sets to the distributed ledger and access to theinstruction sub-sets. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan item of intellectual property and a reporting system that reports ananalytic result based on the operations performed on the distributedledger or the intellectual property and having a smart wrapper for acryptocurrency coin that directs execution of a transaction involvingthe coin to a geographic location based on tax treatment of at least oneof the coin and the transaction in the geographic location. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an item of intellectual property and areporting system that reports an analytic result based on the operationsperformed on the distributed ledger or the intellectual property andhaving a self-executing cryptocurrency coin that commits a transactionupon recognizing a location-based parameter that provides favorable taxtreatment. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an item ofintellectual property and a reporting system that reports an analyticresult based on the operations performed on the distributed ledger orthe intellectual property and having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an item ofintellectual property and a reporting system that reports an analyticresult based on the operations performed on the distributed ledger orthe intellectual property and having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan item of intellectual property and a reporting system that reports ananalytic result based on the operations performed on the distributedledger or the intellectual property and having an expert system thatuses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an item ofintellectual property and a reporting system that reports an analyticresult based on the operations performed on the distributed ledger orthe intellectual property and having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon an understanding of available energy sources to power computingresources to execute the transaction. In embodiments, provided herein isa transaction-enabling system having a distributed ledger that tokenizesan item of intellectual property and a reporting system that reports ananalytic result based on the operations performed on the distributedledger or the intellectual property and having an expert system thatuses machine learning to optimize charging and recharging cycle of arechargeable battery system to provide energy for execution of acryptocurrency transaction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan item of intellectual property and a reporting system that reports ananalytic result based on the operations performed on the distributedledger or the intellectual property and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an item of intellectual property and a reportingsystem that reports an analytic result based on the operations performedon the distributed ledger or the intellectual property and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an item of intellectual property and areporting system that reports an analytic result based on the operationsperformed on the distributed ledger or the intellectual property andhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a cryptocurrency transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan item of intellectual property and a reporting system that reports ananalytic result based on the operations performed on the distributedledger or the intellectual property and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing social network data sources and executes acryptocurrency transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an item of intellectual property and areporting system that reports an analytic result based on the operationsperformed on the distributed ledger or the intellectual property andhaving an expert system that predicts a forward market price in anenergy market based on an understanding obtained by analyzing Internetof Things data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan item of intellectual property and a reporting system that reports ananalytic result based on the operations performed on the distributedledger or the intellectual property and having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an item of intellectual property and areporting system that reports an analytic result based on the operationsperformed on the distributed ledger or the intellectual property andhaving an expert system that predicts a forward market price in a marketfor computing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan item of intellectual property and a reporting system that reports ananalytic result based on the operations performed on the distributedledger or the intellectual property and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan item of intellectual property and a reporting system that reports ananalytic result based on the operations performed on the distributedledger or the intellectual property and having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an item ofintellectual property and a reporting system that reports an analyticresult based on the operations performed on the distributed ledger orthe intellectual property and having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an item of intellectual property and areporting system that reports an analytic result based on the operationsperformed on the distributed ledger or the intellectual property andhaving an expert system that predicts a forward market price in a marketfor advertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan item of intellectual property and a reporting system that reports ananalytic result based on the operations performed on the distributedledger or the intellectual property and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an item of intellectual property and areporting system that reports an analytic result based on the operationsperformed on the distributed ledger or the intellectual property andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan item of intellectual property and a reporting system that reports ananalytic result based on the operations performed on the distributedledger or the intellectual property and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes an item of intellectual property anda reporting system that reports an analytic result based on theoperations performed on the distributed ledger or the intellectualproperty and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thattokenizes an item of intellectual property and a reporting system thatreports an analytic result based on the operations performed on thedistributed ledger or the intellectual property and having a machinethat automatically forecasts forward market pricing of energy pricesbased on information collected from business entity behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an item ofintellectual property and a reporting system that reports an analyticresult based on the operations performed on the distributed ledger orthe intellectual property and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an item of intellectual property and areporting system that reports an analytic result based on the operationsperformed on the distributed ledger or the intellectual property andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan item of intellectual property and a reporting system that reports ananalytic result based on the operations performed on the distributedledger or the intellectual property and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes an item of intellectual property anda reporting system that reports an analytic result based on theoperations performed on the distributed ledger or the intellectualproperty and having a machine that automatically forecasts forwardmarket pricing of energy prices based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan item of intellectual property and a reporting system that reports ananalytic result based on the operations performed on the distributedledger or the intellectual property and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an item of intellectual property and areporting system that reports an analytic result based on the operationsperformed on the distributed ledger or the intellectual property andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an item ofintellectual property and a reporting system that reports an analyticresult based on the operations performed on the distributed ledger orthe intellectual property and having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an item of intellectual property and areporting system that reports an analytic result based on the operationsperformed on the distributed ledger or the intellectual property andhaving an expert system that predicts a forward market price in a marketfor spectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan item of intellectual property and a reporting system that reports ananalytic result based on the operations performed on the distributedledger or the intellectual property and having an intelligent agent thatis configured to solicit the attention resources of another externalintelligent agent. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan item of intellectual property and a reporting system that reports ananalytic result based on the operations performed on the distributedledger or the intellectual property and having a machine thatautomatically purchases attention resources in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an item ofintellectual property and a reporting system that reports an analyticresult based on the operations performed on the distributed ledger orthe intellectual property and having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an item of intellectual property and areporting system that reports an analytic result based on the operationsperformed on the distributed ledger or the intellectual property andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a likelihoodof a facility production outcome. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan item of intellectual property and a reporting system that reports ananalytic result based on the operations performed on the distributedledger or the intellectual property and having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a facility production outcome. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an item of intellectual property and areporting system that reports an analytic result based on the operationsperformed on the distributed ledger or the intellectual property andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that tokenizes an item of intellectual property anda reporting system that reports an analytic result based on theoperations performed on the distributed ledger or the intellectualproperty and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizeprovisioning and allocation of energy and compute resources to produce afavorable facility resource output selection among a set of availableoutputs. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that tokenizes an item ofintellectual property and a reporting system that reports an analyticresult based on the operations performed on the distributed ledger orthe intellectual property and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan item of intellectual property and a reporting system that reports ananalytic result based on the operations performed on the distributedledger or the intellectual property and having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thattokenizes an item of intellectual property and a reporting system thatreports an analytic result based on the operations performed on thedistributed ledger or the intellectual property and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thattokenizes an item of intellectual property and a reporting system thatreports an analytic result based on the operations performed on thedistributed ledger or the intellectual property and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan item of intellectual property and a reporting system that reports ananalytic result based on the operations performed on the distributedledger or the intellectual property and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to atleast one of an input resource, a facility resource, an output parameterand an external condition related to the output of the facility. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that tokenizes an item of intellectual property and areporting system that reports an analytic result based on the operationsperformed on the distributed ledger or the intellectual property andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of input resources. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an item of intellectual property and a reportingsystem that reports an analytic result based on the operations performedon the distributed ledger or the intellectual property and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of facility resources. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thattokenizes an item of intellectual property and a reporting system thatreports an analytic result based on the operations performed on thedistributed ledger or the intellectual property and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that tokenizesan item of intellectual property and a reporting system that reports ananalytic result based on the operations performed on the distributedledger or the intellectual property and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to autilization parameter for the output of the facility. In embodiments,provided herein is a transaction-enabling system having a distributedledger that tokenizes an item of intellectual property and a reportingsystem that reports an analytic result based on the operations performedon the distributed ledger or the intellectual property and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that aggregates a set of instructions, where anoperation on the distributed ledger adds at least one instruction to apre-existing set of instructions to provide a modified set ofinstructions. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that aggregates a set ofinstructions, where an operation on the distributed ledger adds at leastone instruction to a pre-existing set of instructions to provide amodified set of instructions and having a smart wrapper for managementof a distributed ledger that aggregates sets of instructions, where thesmart wrapper manages allocation of instruction sub-sets to thedistributed ledger and access to the instruction sub-sets. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that aggregates a set of instructions, where anoperation on the distributed ledger adds at least one instruction to apre-existing set of instructions to provide a modified set ofinstructions and having a smart wrapper for a cryptocurrency coin thatdirects execution of a transaction involving the coin to a geographiclocation based on tax treatment of at least one of the coin and thetransaction in the geographic location. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thataggregates a set of instructions, where an operation on the distributedledger adds at least one instruction to a pre-existing set ofinstructions to provide a modified set of instructions and having aself-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that aggregates a set ofinstructions, where an operation on the distributed ledger adds at leastone instruction to a pre-existing set of instructions to provide amodified set of instructions and having an expert system that usesmachine learning to optimize the execution of cryptocurrencytransactions based on tax status. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesa set of instructions, where an operation on the distributed ledger addsat least one instruction to a pre-existing set of instructions toprovide a modified set of instructions and having an expert system thataggregates regulatory information covering cryptocurrency transactionsand automatically selects a jurisdiction for an operation based on theregulatory information. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesa set of instructions, where an operation on the distributed ledger addsat least one instruction to a pre-existing set of instructions toprovide a modified set of instructions and having an expert system thatuses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that aggregates a set ofinstructions, where an operation on the distributed ledger adds at leastone instruction to a pre-existing set of instructions to provide amodified set of instructions and having an expert system that usesmachine learning to optimize the execution of a cryptocurrencytransaction based on an understanding of available energy sources topower computing resources to execute the transaction. In embodiments,provided herein is a transaction-enabling system having a distributedledger that aggregates a set of instructions, where an operation on thedistributed ledger adds at least one instruction to a pre-existing setof instructions to provide a modified set of instructions and having anexpert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thataggregates a set of instructions, where an operation on the distributedledger adds at least one instruction to a pre-existing set ofinstructions to provide a modified set of instructions and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that aggregates a set of instructions, where anoperation on the distributed ledger adds at least one instruction to apre-existing set of instructions to provide a modified set ofinstructions and having an expert system that predicts a forward marketprice in a market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesa set of instructions, where an operation on the distributed ledger addsat least one instruction to a pre-existing set of instructions toprovide a modified set of instructions and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that aggregates a set of instructions, where anoperation on the distributed ledger adds at least one instruction to apre-existing set of instructions to provide a modified set ofinstructions and having an expert system that predicts a forward marketprice in a market based on an understanding obtained by analyzing socialnetwork data sources and executes a cryptocurrency transaction based onthe forward market prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesa set of instructions, where an operation on the distributed ledger addsat least one instruction to a pre-existing set of instructions toprovide a modified set of instructions and having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that aggregates a set of instructions, where anoperation on the distributed ledger adds at least one instruction to apre-existing set of instructions to provide a modified set ofinstructions and having an expert system that predicts a forward marketprice in an energy market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesa set of instructions, where an operation on the distributed ledger addsat least one instruction to a pre-existing set of instructions toprovide a modified set of instructions and having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that aggregates a set ofinstructions, where an operation on the distributed ledger adds at leastone instruction to a pre-existing set of instructions to provide amodified set of instructions and having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that aggregates a set ofinstructions, where an operation on the distributed ledger adds at leastone instruction to a pre-existing set of instructions to provide amodified set of instructions and having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that aggregates a set of instructions, where anoperation on the distributed ledger adds at least one instruction to apre-existing set of instructions to provide a modified set ofinstructions and having an expert system that predicts a forward marketprice in a market for advertising based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thataggregates a set of instructions, where an operation on the distributedledger adds at least one instruction to a pre-existing set ofinstructions to provide a modified set of instructions and having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesa set of instructions, where an operation on the distributed ledger addsat least one instruction to a pre-existing set of instructions toprovide a modified set of instructions and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that aggregates a set of instructions, where anoperation on the distributed ledger adds at least one instruction to apre-existing set of instructions to provide a modified set ofinstructions and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thataggregates a set of instructions, where an operation on the distributedledger adds at least one instruction to a pre-existing set ofinstructions to provide a modified set of instructions and having amachine that automatically forecasts forward market pricing of energycredits based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that aggregates a set ofinstructions, where an operation on the distributed ledger adds at leastone instruction to a pre-existing set of instructions to provide amodified set of instructions and having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that aggregates a set of instructions, where anoperation on the distributed ledger adds at least one instruction to apre-existing set of instructions to provide a modified set ofinstructions and having a machine that automatically forecasts forwardmarket pricing of energy prices based on information collected frombusiness entity behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thataggregates a set of instructions, where an operation on the distributedledger adds at least one instruction to a pre-existing set ofinstructions to provide a modified set of instructions and having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that aggregates a set ofinstructions, where an operation on the distributed ledger adds at leastone instruction to a pre-existing set of instructions to provide amodified set of instructions and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a distributedledger that aggregates a set of instructions, where an operation on thedistributed ledger adds at least one instruction to a pre-existing setof instructions to provide a modified set of instructions and having amachine that automatically forecasts forward market value of computecapability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesa set of instructions, where an operation on the distributed ledger addsat least one instruction to a pre-existing set of instructions toprovide a modified set of instructions and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that aggregates a set of instructions, where anoperation on the distributed ledger adds at least one instruction to apre-existing set of instructions to provide a modified set ofinstructions and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesa set of instructions, where an operation on the distributed ledger addsat least one instruction to a pre-existing set of instructions toprovide a modified set of instructions and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that aggregates a set of instructions, where anoperation on the distributed ledger adds at least one instruction to apre-existing set of instructions to provide a modified set ofinstructions and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesa set of instructions, where an operation on the distributed ledger addsat least one instruction to a pre-existing set of instructions toprovide a modified set of instructions and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing social datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that aggregates a set ofinstructions, where an operation on the distributed ledger adds at leastone instruction to a pre-existing set of instructions to provide amodified set of instructions and having an intelligent agent that isconfigured to solicit the attention resources of another externalintelligent agent. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesa set of instructions, where an operation on the distributed ledger addsat least one instruction to a pre-existing set of instructions toprovide a modified set of instructions and having a machine thatautomatically purchases attention resources in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that aggregates a set ofinstructions, where an operation on the distributed ledger adds at leastone instruction to a pre-existing set of instructions to provide amodified set of instructions and having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that aggregates a set of instructions, where anoperation on the distributed ledger adds at least one instruction to apre-existing set of instructions to provide a modified set ofinstructions and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a likelihood of a facility production outcome. In embodiments,provided herein is a transaction-enabling system having a distributedledger that aggregates a set of instructions, where an operation on thedistributed ledger adds at least one instruction to a pre-existing setof instructions to provide a modified set of instructions and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that aggregates a set ofinstructions, where an operation on the distributed ledger adds at leastone instruction to a pre-existing set of instructions to provide amodified set of instructions and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource utilizationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thataggregates a set of instructions, where an operation on the distributedledger adds at least one instruction to a pre-existing set ofinstructions to provide a modified set of instructions and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having adistributed ledger that aggregates a set of instructions, where anoperation on the distributed ledger adds at least one instruction to apre-existing set of instructions to provide a modified set ofinstructions and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesa set of instructions, where an operation on the distributed ledger addsat least one instruction to a pre-existing set of instructions toprovide a modified set of instructions and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a distributed ledger thataggregates a set of instructions, where an operation on the distributedledger adds at least one instruction to a pre-existing set ofinstructions to provide a modified set of instructions and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a distributed ledger thataggregates a set of instructions, where an operation on the distributedledger adds at least one instruction to a pre-existing set ofinstructions to provide a modified set of instructions and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesa set of instructions, where an operation on the distributed ledger addsat least one instruction to a pre-existing set of instructions toprovide a modified set of instructions and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that aggregates a set ofinstructions, where an operation on the distributed ledger adds at leastone instruction to a pre-existing set of instructions to provide amodified set of instructions and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of inputresources. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that aggregates a set ofinstructions, where an operation on the distributed ledger adds at leastone instruction to a pre-existing set of instructions to provide amodified set of instructions and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a distributed ledger that aggregates a set ofinstructions, where an operation on the distributed ledger adds at leastone instruction to a pre-existing set of instructions to provide amodified set of instructions and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to an output parameter.In embodiments, provided herein is a transaction-enabling system havinga distributed ledger that aggregates a set of instructions, where anoperation on the distributed ledger adds at least one instruction to apre-existing set of instructions to provide a modified set ofinstructions and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having a distributed ledger that aggregatesa set of instructions, where an operation on the distributed ledger addsat least one instruction to a pre-existing set of instructions toprovide a modified set of instructions and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga smart wrapper for management of a distributed ledger that aggregatessets of instructions, where the smart wrapper manages allocation ofinstruction sub-sets to the distributed ledger and access to theinstruction sub-sets. In embodiments, provided herein is atransaction-enabling system having a smart wrapper for management of adistributed ledger that aggregates sets of instructions, where the smartwrapper manages allocation of instruction sub-sets to the distributedledger and access to the instruction sub-sets and having a smart wrapperfor a cryptocurrency coin that directs execution of a transactioninvolving the coin to a geographic location based on tax treatment of atleast one of the coin and the transaction in the geographic location. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for management of a distributed ledger that aggregatessets of instructions, where the smart wrapper manages allocation ofinstruction sub-sets to the distributed ledger and access to theinstruction sub-sets and having a self-executing cryptocurrency cointhat commits a transaction upon recognizing a location-based parameterthat provides favorable tax treatment. In embodiments, provided hereinis a transaction-enabling system having a smart wrapper for managementof a distributed ledger that aggregates sets of instructions, where thesmart wrapper manages allocation of instruction sub-sets to thedistributed ledger and access to the instruction sub-sets and having anexpert system that uses machine learning to optimize the execution ofcryptocurrency transactions based on tax status. In embodiments,provided herein is a transaction-enabling system having a smart wrapperfor management of a distributed ledger that aggregates sets ofinstructions, where the smart wrapper manages allocation of instructionsub-sets to the distributed ledger and access to the instructionsub-sets and having an expert system that aggregates regulatoryinformation covering cryptocurrency transactions and automaticallyselects a jurisdiction for an operation based on the regulatoryinformation. In embodiments, provided herein is a transaction-enablingsystem having a smart wrapper for management of a distributed ledgerthat aggregates sets of instructions, where the smart wrapper managesallocation of instruction sub-sets to the distributed ledger and accessto the instruction sub-sets and having an expert system that usesmachine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source. In embodiments, provided herein is a transaction-enablingsystem having a smart wrapper for management of a distributed ledgerthat aggregates sets of instructions, where the smart wrapper managesallocation of instruction sub-sets to the distributed ledger and accessto the instruction sub-sets and having an expert system that usesmachine learning to optimize the execution of a cryptocurrencytransaction based on an understanding of available energy sources topower computing resources to execute the transaction. In embodiments,provided herein is a transaction-enabling system having a smart wrapperfor management of a distributed ledger that aggregates sets ofinstructions, where the smart wrapper manages allocation of instructionsub-sets to the distributed ledger and access to the instructionsub-sets and having an expert system that uses machine learning tooptimize charging and recharging cycle of a rechargeable battery systemto provide energy for execution of a cryptocurrency transaction. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for management of a distributed ledger that aggregatessets of instructions, where the smart wrapper manages allocation ofinstruction sub-sets to the distributed ledger and access to theinstruction sub-sets and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a smart wrapper for management of adistributed ledger that aggregates sets of instructions, where the smartwrapper manages allocation of instruction sub-sets to the distributedledger and access to the instruction sub-sets and having an expertsystem that predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for management of a distributed ledger that aggregatessets of instructions, where the smart wrapper manages allocation ofinstruction sub-sets to the distributed ledger and access to theinstruction sub-sets and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a smart wrapperfor management of a distributed ledger that aggregates sets ofinstructions, where the smart wrapper manages allocation of instructionsub-sets to the distributed ledger and access to the instructionsub-sets and having an expert system that predicts a forward marketprice in a market based on an understanding obtained by analyzing socialnetwork data sources and executes a cryptocurrency transaction based onthe forward market prediction. In embodiments, provided herein is atransaction-enabling system having a smart wrapper for management of adistributed ledger that aggregates sets of instructions, where the smartwrapper manages allocation of instruction sub-sets to the distributedledger and access to the instruction sub-sets and having an expertsystem that predicts a forward market price in an energy market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for management of a distributed ledger that aggregatessets of instructions, where the smart wrapper manages allocation ofinstruction sub-sets to the distributed ledger and access to theinstruction sub-sets and having an expert system that predicts a forwardmarket price in an energy market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having a smart wrapper for management of adistributed ledger that aggregates sets of instructions, where the smartwrapper manages allocation of instruction sub-sets to the distributedledger and access to the instruction sub-sets and having an expertsystem that predicts a forward market price in a market for computingresources based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a smart wrapper for management of adistributed ledger that aggregates sets of instructions, where the smartwrapper manages allocation of instruction sub-sets to the distributedledger and access to the instruction sub-sets and having an expertsystem that predicts a forward market price in a market for spectrum ornetwork bandwidth based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a smart wrapper for management of adistributed ledger that aggregates sets of instructions, where the smartwrapper manages allocation of instruction sub-sets to the distributedledger and access to the instruction sub-sets and having an expertsystem that predicts a forward market price in a market for computingresources based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a smart wrapper for management of a distributed ledgerthat aggregates sets of instructions, where the smart wrapper managesallocation of instruction sub-sets to the distributed ledger and accessto the instruction sub-sets and having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for management of a distributed ledger that aggregatessets of instructions, where the smart wrapper manages allocation ofinstruction sub-sets to the distributed ledger and access to theinstruction sub-sets and having an expert system that predicts a forwardmarket price in a market for advertising based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a smart wrapperfor management of a distributed ledger that aggregates sets ofinstructions, where the smart wrapper manages allocation of instructionsub-sets to the distributed ledger and access to the instructionsub-sets and having a machine that automatically forecasts forwardmarket pricing of energy prices based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a smart wrapper for managementof a distributed ledger that aggregates sets of instructions, where thesmart wrapper manages allocation of instruction sub-sets to thedistributed ledger and access to the instruction sub-sets and having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a smart wrapper for management of a distributed ledgerthat aggregates sets of instructions, where the smart wrapper managesallocation of instruction sub-sets to the distributed ledger and accessto the instruction sub-sets and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a smart wrapperfor management of a distributed ledger that aggregates sets ofinstructions, where the smart wrapper manages allocation of instructionsub-sets to the distributed ledger and access to the instructionsub-sets and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a smart wrapper for managementof a distributed ledger that aggregates sets of instructions, where thesmart wrapper manages allocation of instruction sub-sets to thedistributed ledger and access to the instruction sub-sets and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a smart wrapper for management of a distributed ledgerthat aggregates sets of instructions, where the smart wrapper managesallocation of instruction sub-sets to the distributed ledger and accessto the instruction sub-sets and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for management of a distributed ledger that aggregatessets of instructions, where the smart wrapper manages allocation ofinstruction sub-sets to the distributed ledger and access to theinstruction sub-sets and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom business entity behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets and havinga machine that automatically forecasts forward market value of computecapability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a smart wrapper for management of adistributed ledger that aggregates sets of instructions, where the smartwrapper manages allocation of instruction sub-sets to the distributedledger and access to the instruction sub-sets and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for management of a distributed ledger that aggregatessets of instructions, where the smart wrapper manages allocation ofinstruction sub-sets to the distributed ledger and access to theinstruction sub-sets and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets and havinga machine that automatically forecasts forward market pricing of energycredits based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a smart wrapper for management of a distributed ledgerthat aggregates sets of instructions, where the smart wrapper managesallocation of instruction sub-sets to the distributed ledger and accessto the instruction sub-sets and having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for management of a distributed ledger that aggregatessets of instructions, where the smart wrapper manages allocation ofinstruction sub-sets to the distributed ledger and access to theinstruction sub-sets and having an expert system that predicts a forwardmarket price in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing social data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a smart wrapperfor management of a distributed ledger that aggregates sets ofinstructions, where the smart wrapper manages allocation of instructionsub-sets to the distributed ledger and access to the instructionsub-sets and having an intelligent agent that is configured to solicitthe attention resources of another external intelligent agent. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for management of a distributed ledger that aggregatessets of instructions, where the smart wrapper manages allocation ofinstruction sub-sets to the distributed ledger and access to theinstruction sub-sets and having a machine that automatically purchasesattention resources in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having a smart wrapperfor management of a distributed ledger that aggregates sets ofinstructions, where the smart wrapper manages allocation of instructionsub-sets to the distributed ledger and access to the instructionsub-sets and having a fleet of machines that automatically aggregatepurchasing in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a smart wrapper for management of adistributed ledger that aggregates sets of instructions, where the smartwrapper manages allocation of instruction sub-sets to the distributedledger and access to the instruction sub-sets and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a smart wrapper for management of a distributed ledgerthat aggregates sets of instructions, where the smart wrapper managesallocation of instruction sub-sets to the distributed ledger and accessto the instruction sub-sets and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource utilizationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for management of a distributed ledger that aggregatessets of instructions, where the smart wrapper manages allocation ofinstruction sub-sets to the distributed ledger and access to theinstruction sub-sets and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having a smart wrapper for management of adistributed ledger that aggregates sets of instructions, where the smartwrapper manages allocation of instruction sub-sets to the distributedledger and access to the instruction sub-sets and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for management of a distributed ledger that aggregatessets of instructions, where the smart wrapper manages allocation ofinstruction sub-sets to the distributed ledger and access to theinstruction sub-sets and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for management of a distributed ledger that aggregatessets of instructions, where the smart wrapper manages allocation ofinstruction sub-sets to the distributed ledger and access to theinstruction sub-sets and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for management of a distributed ledger that aggregatessets of instructions, where the smart wrapper manages allocation ofinstruction sub-sets to the distributed ledger and access to theinstruction sub-sets and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to at least one of aninput resource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a smart wrapperfor management of a distributed ledger that aggregates sets ofinstructions, where the smart wrapper manages allocation of instructionsub-sets to the distributed ledger and access to the instructionsub-sets and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for management of a distributed ledger that aggregatessets of instructions, where the smart wrapper manages allocation ofinstruction sub-sets to the distributed ledger and access to theinstruction sub-sets and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a smart wrapper for management of a distributed ledgerthat aggregates sets of instructions, where the smart wrapper managesallocation of instruction sub-sets to the distributed ledger and accessto the instruction sub-sets and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to an output parameter.In embodiments, provided herein is a transaction-enabling system havinga smart wrapper for management of a distributed ledger that aggregatessets of instructions, where the smart wrapper manages allocation ofinstruction sub-sets to the distributed ledger and access to theinstruction sub-sets and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a utilizationparameter for the output of the facility. In embodiments, providedherein is a transaction-enabling system having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of parametersreceived from a digital twin for the facility.

Referencing FIG. 57, an example transaction-enabling system 5700includes a controller 5702 configured to interpret a resourceutilization requirement 5704 for a task system 5706 having at least onetask, such as a compute task, a network task, or a core task. Theexample controller 5702 is further configured to interpret a number ofexternal data sources 5708, where the external data sources 5708 includeat least one data source outside of the task system 5706. The examplecontroller 5702 operates an expert system 5710 that predicts a forwardmarket price 5712 in response to the resource utilization requirement5704 and the external data sources 5708, and that executes a transaction5714 on a resource market 5716 in response to the predicted forwardmarket price 5712. Example and non-limiting external data sources 5708includes one or more sources such as IoT data sources and social mediadata sources. Example and non-limiting operations to predict the forwardmarket price include determining indicators on social media that relateto forward market prices (e.g., conversation types, keyword occurrences,sharing topic indicators, and/or event indicators such as cultural,sporting, or internet events), determining external data indicators thatrelate to forward market prices (e.g., data from industrial systems thatparticipate in a market that may affect resource prices, news eventsthat may affect resource prices, published or proprietary data such asorders, economic indicators, demographic indicators, or the like thatmay affect resource prices). All examples for external data describedherein are non-limiting examples to illustrate certain principles of thepresent disclosure.

In certain embodiments, the resource requirement(s) 5704 relate to acompute resource, a network bandwidth resource, a spectrum resource, adata storage resource, an energy resource, and/or an energy creditresource. In certain embodiments, the resource market 5716 may be aforward market and/or a spot market for a resource. In certainembodiments, the transactions 5714 may be on a forward market and/or aspot market. In certain embodiments, the transactions 5714 may include apurchase or a sale of the resource, and may further include combinations(e.g., a purchase on a spot market and a sale on a forward market).

An example system 5700 includes the resource utilization requirement5704 including a requirement for a first resource, and where the forwardmarket price 5712 is a forward price prediction for the first resource,for a second resource, and/or for both resources. For example, thesecond resource may be a resource that can be substituted for the firstresource. The substitution may be direct, such as where multiple typesof fuel available to power a system, or where multiple task deliverycomponents are available that consume distinct types of resources.Additionally or alternatively, the substitution may be indirect, such aswhere operational changes can trade out one type of resource utilizationfor another—e.g., trading compute resources for network resources (e.g.,outsourcing compute tasks, effectively making them network tasks for thetask system), changing a process to reduce a type of resource (e.g.,operating a less efficient algorithm that results in increased datastorage but reduced computing; operating a process at a lower rate ortemperature, reducing energy usage for the direct operation, butincreasing energy usage due to the increased time of operating afacility performing the process, and further where the energy usage forthe direct operation and for operating the facility may be distincttypes of energy). The examples of the first resource and second resourceare non-limiting and provided for illustration. An example system 5700further includes the controller 5702 configured to operate the expertsystem 5710 to determine a substitution cost 5718 of the secondresource, and to execute the transaction 5714 in response to thesubstitution cost 5718—which may include purchasing or selling the firstresource and/or the second resource, or both, and/or varying thetransactions 5714 over time. In certain embodiments, the substitutioncost 5718 is determined from the forward market price(s) 5712 of thefirst and second resource, the spot market price(s) of the first andsecond resource, and/or from other system costs or effects that mayresult from utilization changes between the first and second resource,such as operational change costs to the task system 5706 (e.g., time tocomplete tasks; facility changes such as personnel and/or equipmentchanges due to operating with either the first or second resources;and/or secondary effects such as consumption of energy credits,exceedance of a capacity of a component of the task system, and/orchanges to a quality of products or services provided by the task system5706).

Referencing FIG. 58, an example procedure 5800 includes an operation5802 to interpret a resource utilization requirement for a task systemhaving at least one of a compute task, a network task, or a core task;an operation 5804 to interpret a number of external data sources, wherethe number of external data sources include at least one data sourceoutside of the task system. The example procedure 5800 further includesan operation 5806 to operate an expert system (and/or AI or machinelearning system) to predict a forward market price for a resource inresponse to the resource utilization requirement and the number ofexternal data sources; and an operation 5808 to execute a transaction ona resource market in response to the predicted forward market price.

Referencing FIG. 59, an example procedure 5900 includes an operation5902 to determine a second resource that can substitute for the firstresource, an operation 5904 to consider the forward market price of oneor more resources, and/or to further consider the operational costchange between the first and second resources, and an operation 5906 toexecute a transaction on a resource market of the first resource or thesecond resource in response to the substitution cost and/or operationalcost change between the first and second resources.

With further reference to FIG. 57, the example system 5700 includes thecontroller 5702 configured to interpret a resource utilizationrequirement 5704 for a task system 5706, and to interpret a behavioraldata source 5720; to operate a machine (e.g., an expert system 5710,and/or an AI or machine learning component) to forecast a forward marketprice 5712 for a resource in response to the resource utilizationrequirement 5704 and the behavioral data source 5720, and to perform oneof adjusting an operation of the task system (e.g., providingoperational adjustments 5722) or executing a transaction 5714 inresponse to the forecast of the forward market price 5712 for theresource. Example and non-limiting behavioral data sources 5720 includedata sources such as: an automated agent behavioral data source, a humanbehavioral data source, and/or a business entity behavioral data source.In certain embodiments, the controller 5702 is further configured tooperation the machine to provide operational adjustments 5722 such as:adjusting operations of the task system 5706 to increase or reduce theresource utilization requirement 5704; adjusting operations of the tasksystem 5706 to time shift at least a portion of the resource utilizationrequirement 5704; adjusting operations of the task system 5706 tosubstitute utilization of a first resource for utilization of a secondresource; and/or accessing an external provider to provide at least aportion of at least one of the compute task, the network task, or thecore task.

Referencing FIG. 60, an example procedure 6000 includes an operation6002 to interpret a resource utilization requirement for a task system,an operation 6004 to interpret a behavioral data source, an operation6006 to operate a machine to forecast a forward market value for aresource in response to the resource utilization requirement and thebehavioral data source, and an operation 6008 to adjust an operation ofthe task system and/or to execute a transaction in response to theforecast of the forward market value for the resource. In certainembodiments, procedure 6000 further includes operations such as thosedescribed in reference to FIG. 59 to further consider substitution costsfor a first and second resource, and to perform operations 6008 furtherin response to the substitution costs for the first and second resourceand adjust an operation of the task system and/or execute a transactionin response to the forward market price.

With further reference to FIG. 57, an example system 5700 includes thecontroller 5702 configured to interpret a resource utilizationrequirement 5704 for a task system 5706, to interpret a number ofexternal data sources 5708, to operate an expert system 5710 to predicta forward market price 5712 for a resource in response to the resourceutilization requirement 5704 and the number of external data sources,and to execute a cryptocurrency transaction (e.g., transaction 5714) ona resource market 5716 in response to the predicted forward marketprice.

With reference to FIG. 61, an example procedure 6100 includes anoperation 6102 to interpret a resource utilization requirement for atask system, an operation 6104 to interpret a number of external datasources, an operation 6106 to operate an expert system to predict aforward market price for a resource in response to the resourceutilization requirement and the number of external data sources, and anoperation 6108 to execute a cryptocurrency transaction on a resourcemarket in response to the predicted forward market price. Operations ofthe procedure 6100, and further for operations of any systems orprocedures throughout the present disclosure that determine a forwardmarket price for a resource may additionally or alternatively includedetermining a forward market price for a number of forward market timeframes, including a predicted price for a spot market at one or morefuture time values. Operations of the procedure 6100, and further foroperations of any system or procedures throughout the present disclosurethat perform one or more transactions or operational adjustments inresponse to a forward market price include operations provide for animproved cost of operation of a task system, and/or to provide for anincreased capability (e.g., quality, volume, and/or completion time) ofa task system. In certain embodiments, procedure 6100 further includesoperations such as those described in reference to FIG. 59 to furtherconsider substitution costs for a first and second resource, and toperform operations 6108 further in response to the substitution costsfor the first and second resource.

In embodiments, provided herein is a transaction-enabling system havingan expert system that predicts a forward market price in a market basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction andhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing social network datasources and executes a cryptocurrency transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction and having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction andhaving an expert system that predicts a forward market price in anenergy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction and having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having an expert system that predicts a forwardmarket price in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction andhaving an expert system that predicts a forward market price in a marketfor computing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction and having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction andhaving an expert system that predicts a forward market price in a marketfor advertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havingan expert system that predicts a forward market price in a market basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havingan expert system that predicts a forward market price in a market basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected frombusiness entity behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom human behavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction andhaving an expert system that predicts a forward market price in a marketfor spectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction and having an intelligent agentthat is configured to solicit the attention resources of anotherexternal intelligent agent. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction and having a machine thatautomatically purchases attention resources in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having a fleet of machines that automaticallyaggregate purchasing in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a facility production outcome. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havingan expert system that predicts a forward market price in a market basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize requisition and provisioning of availableenergy and compute resources to produce a favorable facility inputresource profile among a set of available profiles. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize selectionand configuration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to generate an indication that a current or prospectivecustomer should be contacted about an output that can be provided by thefacility. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of input resources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of facility resources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of parametersreceived from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havingan expert system that predicts a forward market price in a market basedon an understanding obtained by analyzing social network data sourcesand executes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havingan expert system that predicts a forward market price in a market basedon an understanding obtained by analyzing Internet of Things datasources and executes a cryptocurrency transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing social network data sources and executes acryptocurrency transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havingan expert system that predicts a forward market price in an energymarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction and having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havingan expert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction and having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction and having an expert system that predicts a forward marketprice in a market for advertising based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction and having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market value of computecapability based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market pricing of networkspectrum based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected frombusiness entity behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction and having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction and having a machinethat automatically forecasts forward market value of compute capabilitybased on information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havingan expert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction and having an intelligent agent that isconfigured to solicit the attention resources of another externalintelligent agent. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction and having a machine that automaticallypurchases attention resources in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga fleet of machines that automatically aggregate purchasing in a forwardmarket for attention. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a likelihood of a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a facility production outcome. In embodiments, provided hereinis a transaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource utilizationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize requisition andprovisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize selection and configuration of an artificialintelligence system to produce a favorable facility output profile amonga set of available artificial intelligence systems and configurations.In embodiments, provided herein is a transaction-enabling system havingan expert system that predicts a forward market price in a market basedon an understanding obtained by analyzing social network data sourcesand executes a transaction based on the forward market prediction andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to at least one of an input resource, a facilityresource, an output parameter and an external condition related to theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to a setof input resources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to a setof facility resources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to anoutput parameter. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to autilization parameter for the output of the facility. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of parametersreceived from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havingan expert system that predicts a forward market price in a market basedon an understanding obtained by analyzing Internet of Things datasources and executes a cryptocurrency transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction and having an expertsystem that predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a cryptocurrency transaction based onthe forward market prediction and having an expert system that predictsa forward market price in an energy market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a cryptocurrency transaction based on the forward marketprediction and having an expert system that predicts a forward marketprice in an energy market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction and having an expertsystem that predicts a forward market price in a market for computingresources based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction and having an expertsystem that predicts a forward market price in a market for spectrum ornetwork bandwidth based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction and having an expertsystem that predicts a forward market price in a market for computingresources based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a cryptocurrency transaction based onthe forward market prediction and having an expert system that predictsa forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a cryptocurrency transaction based on the forward marketprediction and having an expert system that predicts a forward marketprice in a market for advertising based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction and having a machinethat automatically forecasts forward market pricing of energy pricesbased on information collected from automated agent behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a cryptocurrency transaction based onthe forward market prediction and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a cryptocurrency transaction based on the forward marketprediction and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction and having a machinethat automatically forecasts forward market value of compute capabilitybased on information collected from automated agent behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a cryptocurrency transaction based onthe forward market prediction and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a cryptocurrency transaction based on the forward marketprediction and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected frombusiness entity behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction and having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from business entity behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a cryptocurrency transaction based onthe forward market prediction and having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a cryptocurrency transaction based on the forward marketprediction and having a machine that automatically forecasts forwardmarket pricing of energy prices based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction and having a machinethat automatically forecasts forward market pricing of network spectrumbased on information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a cryptocurrency transaction based on the forward marketprediction and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction and having a machinethat automatically forecasts forward market value of compute capabilitybased on information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a cryptocurrency transaction based on the forward marketprediction and having an expert system that predicts a forward marketprice in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing social data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a cryptocurrency transaction based on the forward marketprediction and having an intelligent agent that is configured to solicitthe attention resources of another external intelligent agent. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a cryptocurrency transaction based on the forward marketprediction and having a machine that automatically purchases attentionresources in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction andhaving a fleet of machines that automatically aggregate purchasing in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a cryptocurrency transaction based onthe forward market prediction and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource utilizationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a cryptocurrency transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a cryptocurrency transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a cryptocurrency transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a cryptocurrency transaction based on the forward marketprediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a cryptocurrency transaction based on the forward marketprediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a cryptocurrency transaction based on the forward marketprediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a cryptocurrency transaction based on the forward marketprediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to an output parameter. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a cryptocurrency transaction based on the forward marketprediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havingan expert system that predicts a forward market price in a market basedon an understanding obtained by analyzing social network data sourcesand executes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a cryptocurrency transaction based on theforward market prediction and having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction and having an expert system that predicts a forward marketprice in an energy market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a cryptocurrencytransaction based on the forward market prediction and having an expertsystem that predicts a forward market price in a market for computingresources based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a cryptocurrencytransaction based on the forward market prediction and having an expertsystem that predicts a forward market price in a market for spectrum ornetwork bandwidth based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a cryptocurrencytransaction based on the forward market prediction and having an expertsystem that predicts a forward market price in a market for computingresources based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a cryptocurrency transaction based on theforward market prediction and having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction and having an expert system that predicts a forward marketprice in a market for advertising based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a cryptocurrencytransaction based on the forward market prediction and having a machinethat automatically forecasts forward market pricing of energy pricesbased on information collected from automated agent behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a cryptocurrency transaction based on theforward market prediction and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a cryptocurrencytransaction based on the forward market prediction and having a machinethat automatically forecasts forward market value of compute capabilitybased on information collected from automated agent behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a cryptocurrency transaction based on theforward market prediction and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected frombusiness entity behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a cryptocurrencytransaction based on the forward market prediction and having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from business entity behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a cryptocurrency transaction based on theforward market prediction and having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction and having a machine that automatically forecasts forwardmarket pricing of energy prices based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a cryptocurrencytransaction based on the forward market prediction and having a machinethat automatically forecasts forward market pricing of network spectrumbased on information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a cryptocurrencytransaction based on the forward market prediction and having a machinethat automatically forecasts forward market value of compute capabilitybased on information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction and having an expert system that predicts a forward marketprice in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing social data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction and having an intelligent agent that is configured to solicitthe attention resources of another external intelligent agent. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction and having a machine that automatically purchases attentionresources in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing social network data sources and executes acryptocurrency transaction based on the forward market prediction andhaving a fleet of machines that automatically aggregate purchasing in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a cryptocurrencytransaction based on the forward market prediction and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a cryptocurrencytransaction based on the forward market prediction and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a cryptocurrency transaction based on theforward market prediction and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize provisioning and allocation of energy and compute resourcesto produce a favorable facility resource utilization profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a cryptocurrencytransaction based on the forward market prediction and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a cryptocurrencytransaction based on the forward market prediction and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to an output parameter. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a cryptocurrencytransaction based on the forward market prediction and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havingan expert system that predicts a forward market price in an energymarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction and having an expertsystem that predicts a forward market price in an energy market based onan understanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having an expert system that predicts a forward marketprice in a market for computing resources based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havingan expert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction and having an expertsystem that predicts a forward market price in a market for computingresources based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction and having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having an expert system that predicts a forward marketprice in a market for advertising based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction and having a machinethat automatically forecasts forward market pricing of energy pricesbased on information collected from automated agent behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction and having a machinethat automatically forecasts forward market value of compute capabilitybased on information collected from automated agent behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market pricing of networkspectrum based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market value of computecapability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction and having a machinethat automatically forecasts forward market pricing of energy pricesbased on information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction and having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction and having an expertsystem that predicts a forward market price in a market for spectrum ornetwork bandwidth based on an understanding obtained by analyzing socialdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction and having an intelligent agent that isconfigured to solicit the attention resources of another externalintelligent agent. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction and having a machinethat automatically purchases attention resources in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction and having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a likelihood of a facility production outcome. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize provisioning and allocation of energy and compute resourcesto produce a favorable facility resource utilization profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of input resources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of facility resources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a utilization parameter for the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of parameters received from a digital twin for thefacility.

In embodiments, provided herein is a transaction-enabling system havingan expert system that predicts a forward market price in an energymarket based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction and having an expert system that predicts a forwardmarket price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction and having an expert system that predicts a forward marketprice in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction and having an expert system that predicts a forward marketprice in a market for computing resources based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in an energy market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havingan expert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction and having an expertsystem that predicts a forward market price in a market for advertisingbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction and having a machine that automatically forecastsforward market pricing of energy prices based on information collectedfrom automated agent behavioral data sources. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market pricing of networkspectrum based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom automated agent behavioral data sources. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market value of computecapability based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction and having a machinethat automatically forecasts forward market pricing of energy pricesbased on information collected from business entity behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in an energy market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market pricing of energycredits based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in an energy market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market pricing of energycredits based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havingan expert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction and having anintelligent agent that is configured to solicit the attention resourcesof another external intelligent agent. In embodiments, provided hereinis a transaction-enabling system having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction and having a machinethat automatically purchases attention resources in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction and having a fleet of machines that automaticallyaggregate purchasing in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in an energy market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in an energy market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of input resources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in an energy market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of facility resources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in an energy market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a utilization parameter for the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price inan energy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of parameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havingan expert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havingan expert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havingan expert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havingan expert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havingan expert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for computing resources based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havingan expert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for computing resources based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havingan expert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected frombusiness entity behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for computing resources based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havingan expert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havingan intelligent agent that is configured to solicit the attentionresources of another external intelligent agent. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a machine that automatically purchases attentionresources in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a fleet of machines that automatically aggregatepurchasing in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a likelihood of a facility production outcome. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a facility production outcome. In embodiments, provided hereinis a transaction-enabling system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havingan expert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize provisioning and allocation of energy and compute resourcesto produce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize requisition andprovisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for computing resources based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto generate an indication that a current or prospective customer shouldbe contacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to at least one of aninput resource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for computing resources based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a utilization parameter for the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for computing resources based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havingan expert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having an expert system that predicts a forward marketprice in a market for computing resources based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having an expert system that predicts a forwardmarket price in a market for advertising based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having an expert system that predicts a forwardmarket price in a market for advertising based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having a machine that automatically forecastsforward market pricing of energy prices based on information collectedfrom automated agent behavioral data sources. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom automated agent behavioral data sources. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having a machine that automatically forecastsforward market pricing of energy prices based on information collectedfrom business entity behavioral data sources. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom business entity behavioral data sources. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having a machine that automatically forecastsforward market pricing of energy prices based on information collectedfrom human behavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having an expert system that predicts a forward marketprice in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing social data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having an intelligent agent that is configured tosolicit the attention resources of another external intelligent agent.In embodiments, provided herein is a transaction-enabling system havingan expert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction and having a machine thatautomatically purchases attention resources in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction and having a fleet ofmachines that automatically aggregate purchasing in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a facility production outcome. In embodiments, provided hereinis a transaction-enabling system having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to generate an indication that a current or prospectivecustomer should be contacted about an output that can be provided by thefacility. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of input resources. In embodiments, provided herein isa transaction-enabling system having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of parameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havingan expert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havingan expert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havingan expert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for computing resources based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for computing resources based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected frombusiness entity behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for computing resources based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havingan expert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havingan intelligent agent that is configured to solicit the attentionresources of another external intelligent agent. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction and having a machine that automatically purchases attentionresources in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction and having a fleet of machines that automatically aggregatepurchasing in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a likelihood of a facility production outcome. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a facility production outcome. In embodiments, provided hereinis a transaction-enabling system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havingan expert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize provisioning and allocation of energy and compute resourcesto produce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize requisition andprovisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for computing resources based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto generate an indication that a current or prospective customer shouldbe contacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to at least one of aninput resource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for computing resources based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to anoutput parameter. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a utilization parameter for the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for computing resources based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of parameters received from a digital twinfor the facility.

In embodiments, provided herein is a transaction-enabling system havingan expert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havingan expert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom automated agent behavioral data sources. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market value of computecapability based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom business entity behavioral data sources. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market value of computecapability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom human behavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market value of computecapability based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction and having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing social data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having an intelligent agent that is configured tosolicit the attention resources of another external intelligent agent.In embodiments, provided herein is a transaction-enabling system havingan expert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having a machine that automatically purchasesattention resources in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for advertising basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having a fleet of machines that automatically aggregatepurchasing in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize provisioning and allocation of energy and compute resourcesto produce a favorable facility resource utilization profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for advertising basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to an output parameter. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of parametersreceived from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havingan expert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom automated agent behavioral data sources. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market value of computecapability based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom business entity behavioral data sources. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market value of computecapability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom human behavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga machine that automatically forecasts forward market value of computecapability based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction and having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing social data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction and having an intelligent agent that is configured tosolicit the attention resources of another external intelligent agent.In embodiments, provided herein is a transaction-enabling system havingan expert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction and having a machine that automatically purchasesattention resources in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for advertising basedon an understanding obtained by analyzing social network data sourcesand executes a transaction based on the forward market prediction andhaving a fleet of machines that automatically aggregate purchasing in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize provisioning and allocation of energy and compute resourcesto produce a favorable facility resource utilization profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for advertising basedon an understanding obtained by analyzing social network data sourcesand executes a transaction based on the forward market prediction andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of input resources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for advertising basedon an understanding obtained by analyzing social network data sourcesand executes a transaction based on the forward market prediction andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of facility resources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for advertising basedon an understanding obtained by analyzing social network data sourcesand executes a transaction based on the forward market prediction andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a utilization parameter for the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of parameters received from a digital twin for thefacility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from automatedagent behavioral data sources and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from automated agent behavioral data sources and having amachine that automatically forecasts forward market value of computecapability based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from automated agent behavioral data sources and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from automatedagent behavioral data sources and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected frombusiness entity behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from automated agent behavioral data sources and having amachine that automatically forecasts forward market value of computecapability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from automated agent behavioral data sources and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from automatedagent behavioral data sources and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from automated agent behavioral data sources and having amachine that automatically forecasts forward market value of computecapability based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from automatedagent behavioral data sources and having an expert system that predictsa forward market price in a market for spectrum or network bandwidthbased on an understanding obtained by analyzing social data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources and having an intelligent agent that is configured tosolicit the attention resources of another external intelligent agent.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources and having a machine that automatically purchases attentionresources in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources andhaving a fleet of machines that automatically aggregate purchasing in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from automated agent behavioral data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from automated agent behavioral data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from automatedagent behavioral data sources and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource utilizationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from automated agent behavioral data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of input resources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of facility resources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a utilization parameter for the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from automatedagent behavioral data sources and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of parameters received from a digital twin for thefacility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of networkspectrum based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected fromautomated agent behavioral data sources and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of networkspectrum based on information collected from automated agent behavioraldata sources and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected fromautomated agent behavioral data sources and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from automated agent behavioraldata sources and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected fromautomated agent behavioral data sources and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing social datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected fromautomated agent behavioral data sources and having an intelligent agentthat is configured to solicit the attention resources of anotherexternal intelligent agent. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources andhaving a machine that automatically purchases attention resources in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources andhaving a fleet of machines that automatically aggregate purchasing in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a likelihoodof a facility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a facilityproduction outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of networkspectrum based on information collected from automated agent behavioraldata sources and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize requisitionand provisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected fromautomated agent behavioral data sources and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from automated agent behavioral data sourcesand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize selectionand configuration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from automated agent behavioral data sourcesand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofdetected conditions relating to at least one of an input resource, afacility resource, an output parameter and an external condition relatedto the output of the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of input resources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from automated agent behavioral data sourcesand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from automated agent behavioraldata sources and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to an output parameter. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from automated agent behavioraldata sources and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of parametersreceived from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of energycredits based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from automatedagent behavioral data sources and having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from automated agent behavioraldata sources and having a machine that automatically forecasts forwardmarket pricing of energy prices based on information collected frombusiness entity behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from automated agent behavioral data sources and having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from automatedagent behavioral data sources and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from automated agent behavioral data sourcesand having a machine that automatically forecasts forward market valueof compute capability based on information collected from businessentity behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from automated agent behavioral data sources and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from automatedagent behavioral data sources and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from automated agent behavioraldata sources and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from automated agent behavioral data sources and having amachine that automatically forecasts forward market value of computecapability based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from automatedagent behavioral data sources and having an expert system that predictsa forward market price in a market for spectrum or network bandwidthbased on an understanding obtained by analyzing social data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from automated agent behavioraldata sources and having an intelligent agent that is configured tosolicit the attention resources of another external intelligent agent.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of energycredits based on information collected from automated agent behavioraldata sources and having a machine that automatically purchases attentionresources in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from automated agent behavioral data sourcesand having a fleet of machines that automatically aggregate purchasingin a forward market for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from automated agent behavioral data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from automated agent behavioral data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from automatedagent behavioral data sources and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource utilizationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from automated agent behavioral data sourcesand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from automated agent behavioraldata sources and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from automated agent behavioral data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from automated agent behavioraldata sources and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from automated agent behavioraldata sources and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from automated agent behavioraldata sources and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from automated agent behavioral data sourcesand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofdetected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from automated agent behavioraldata sources and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from automated agent behavioraldata sources and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to an output parameter. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from automated agent behavioraldata sources and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from automated agent behavioral data sources and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market value of computecapability based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from automated agent behavioral data sources andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from automated agent behavioral data sources andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from automated agent behavioral data sources andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from automated agent behavioral data sources andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from automated agent behavioral data sources andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected fromautomated agent behavioral data sources and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market value of computecapability based on information collected from automated agentbehavioral data sources and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from automated agent behavioral data sourcesand having a machine that automatically forecasts forward market valueof compute capability based on information collected from humanbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from automated agent behavioral data sources andhaving an expert system that predicts a forward market price in a marketfor spectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from automated agent behavioral data sources andhaving an intelligent agent that is configured to solicit the attentionresources of another external intelligent agent. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from automated agent behavioral data sourcesand having a machine that automatically purchases attention resources ina forward market for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from automated agent behavioral data sources andhaving a fleet of machines that automatically aggregate purchasing in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from automated agent behavioral data sources andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a likelihoodof a facility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from automated agent behavioral data sources andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a facilityproduction outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from automated agent behavioral data sources andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market value of computecapability based on information collected from automated agentbehavioral data sources and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize provisioning and allocation of energy and compute resourcesto produce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from automated agent behavioral data sources andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize requisitionand provisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected fromautomated agent behavioral data sources and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from automated agent behavioral data sourcesand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize selectionand configuration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from automated agent behavioral data sources andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from automated agent behavioral data sourcesand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofdetected conditions relating to at least one of an input resource, afacility resource, an output parameter and an external condition relatedto the output of the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from automated agent behavioral data sources andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of input resources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from automated agent behavioral data sourcesand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market value of computecapability based on information collected from automated agentbehavioral data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to an output parameter.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market value of computecapability based on information collected from automated agentbehavioral data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a utilizationparameter for the output of the facility. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from automated agent behavioral data sourcesand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofparameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from businessentity behavioral data sources and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from business entity behavioraldata sources and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected frombusiness entity behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from business entity behavioral data sources and having amachine that automatically forecasts forward market value of computecapability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from business entity behavioral data sources and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from businessentity behavioral data sources and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from business entity behavioraldata sources and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from business entity behavioral data sources and having amachine that automatically forecasts forward market value of computecapability based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from businessentity behavioral data sources and having an expert system that predictsa forward market price in a market for spectrum or network bandwidthbased on an understanding obtained by analyzing social data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from business entity behavioraldata sources and having an intelligent agent that is configured tosolicit the attention resources of another external intelligent agent.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from business entity behavioraldata sources and having a machine that automatically purchases attentionresources in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from business entity behavioral data sources andhaving a fleet of machines that automatically aggregate purchasing in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from business entity behavioral data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from business entity behavioral data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from businessentity behavioral data sources and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource utilizationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from business entity behavioral data sources andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from business entity behavioraldata sources and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from business entity behavioral data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from business entity behavioraldata sources and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from business entity behavioraldata sources and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from business entity behavioraldata sources and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from business entity behavioral data sources andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of input resources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from business entity behavioral data sources andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of facility resources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from business entity behavioral data sources andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from business entity behavioral data sources andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a utilization parameter for the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from businessentity behavioral data sources and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of parameters received from a digital twinfor the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of networkspectrum based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from businessentity behavioral data sources and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from business entity behavioral data sourcesand having a machine that automatically forecasts forward market valueof compute capability based on information collected from businessentity behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from businessentity behavioral data sources and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from business entity behavioraldata sources and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from businessentity behavioral data sources and having an expert system that predictsa forward market price in a market for spectrum or network bandwidthbased on an understanding obtained by analyzing social data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from business entity behavioraldata sources and having an intelligent agent that is configured tosolicit the attention resources of another external intelligent agent.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of networkspectrum based on information collected from business entity behavioraldata sources and having a machine that automatically purchases attentionresources in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from business entity behavioral data sourcesand having a fleet of machines that automatically aggregate purchasingin a forward market for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a likelihoodof a facility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a facilityproduction outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of networkspectrum based on information collected from business entity behavioraldata sources and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize requisitionand provisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from businessentity behavioral data sources and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from business entity behavioral data sourcesand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize selectionand configuration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from business entity behavioral data sourcesand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofdetected conditions relating to at least one of an input resource, afacility resource, an output parameter and an external condition relatedto the output of the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of input resources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from business entity behavioral data sourcesand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from business entity behavioraldata sources and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to an output parameter. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from business entity behavioraldata sources and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of parametersreceived from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of energycredits based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from businessentity behavioral data sources and having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from business entity behavioraldata sources and having a machine that automatically forecasts forwardmarket pricing of energy prices based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from business entity behavioral data sources and having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from businessentity behavioral data sources and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from business entity behavioral data sourcesand having a machine that automatically forecasts forward market valueof compute capability based on information collected from humanbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from business entity behavioral data sources and having anexpert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from business entity behavioral data sources and having anintelligent agent that is configured to solicit the attention resourcesof another external intelligent agent. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from business entity behavioral data sources and having amachine that automatically purchases attention resources in a forwardmarket for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from business entity behavioral data sources and having afleet of machines that automatically aggregate purchasing in a forwardmarket for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from business entity behavioral data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from business entity behavioral data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from businessentity behavioral data sources and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource utilizationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from business entity behavioral data sourcesand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from business entity behavioraldata sources and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from business entity behavioral data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from business entity behavioraldata sources and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from business entity behavioraldata sources and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from business entity behavioraldata sources and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from business entity behavioral data sourcesand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofdetected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from business entity behavioraldata sources and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from business entity behavioraldata sources and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to an output parameter. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from business entity behavioraldata sources and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from business entity behavioral data sources and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market value of computecapability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from business entity behavioral data sources andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from businessentity behavioral data sources and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market value of computecapability based on information collected from business entitybehavioral data sources and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sourcesand having a machine that automatically forecasts forward market valueof compute capability based on information collected from humanbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from business entity behavioral data sources andhaving an expert system that predicts a forward market price in a marketfor spectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from business entity behavioral data sources andhaving an intelligent agent that is configured to solicit the attentionresources of another external intelligent agent. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sourcesand having a machine that automatically purchases attention resources ina forward market for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from business entity behavioral data sources andhaving a fleet of machines that automatically aggregate purchasing in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from business entity behavioral data sources andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a likelihoodof a facility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from business entity behavioral data sources andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a facilityproduction outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from business entity behavioral data sources andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market value of computecapability based on information collected from business entitybehavioral data sources and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize provisioning and allocation of energy and compute resourcesto produce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from business entity behavioral data sources andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize requisitionand provisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from businessentity behavioral data sources and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sourcesand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize selectionand configuration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from business entity behavioral data sources andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sourcesand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofdetected conditions relating to at least one of an input resource, afacility resource, an output parameter and an external condition relatedto the output of the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from business entity behavioral data sources andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of input resources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sourcesand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market value of computecapability based on information collected from business entitybehavioral data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to an output parameter.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market value of computecapability based on information collected from business entitybehavioral data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a utilizationparameter for the output of the facility. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sourcesand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofparameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from humanbehavioral data sources and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from human behavioral data sourcesand having a machine that automatically forecasts forward market pricingof energy credits based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from humanbehavioral data sources and having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from human behavioral data sourcesand having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing social data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from human behavioral data sources and having an intelligentagent that is configured to solicit the attention resources of anotherexternal intelligent agent. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from human behavioral data sources and having a machine thatautomatically purchases attention resources in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from humanbehavioral data sources and having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from human behavioral data sourcesand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a likelihoodof a facility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from human behavioral data sources and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from humanbehavioral data sources and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize provisioning and allocation of energy and compute resourcesto produce a favorable facility resource utilization profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from human behavioral data sources and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from human behavioral data sourcesand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize requisitionand provisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from humanbehavioral data sources and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from human behavioral data sources and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from human behavioral data sources and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from human behavioral data sources and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from humanbehavioral data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of inputresources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from humanbehavioral data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from humanbehavioral data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to an output parameter.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from human behavioral data sourcesand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofdetected conditions relating to a utilization parameter for the outputof the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from human behavioral data sources and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of networkspectrum based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from humanbehavioral data sources and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from human behavioral data sources and having amachine that automatically forecasts forward market value of computecapability based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from humanbehavioral data sources and having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing social data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from human behavioral datasources and having an intelligent agent that is configured to solicitthe attention resources of another external intelligent agent. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from human behavioral datasources and having a machine that automatically purchases attentionresources in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from human behavioral data sources and having afleet of machines that automatically aggregate purchasing in a forwardmarket for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from humanbehavioral data sources and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize provisioning and allocation of energy and compute resourcesto produce a favorable facility resource utilization profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from human behavioral datasources and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizerequisition and provisioning of available energy and compute resourcesto produce a favorable facility input resource profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from human behavioral datasources and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizeselection and configuration of an artificial intelligence system toproduce a favorable facility output profile among a set of availableartificial intelligence systems and configurations. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from human behavioral data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from humanbehavioral data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of inputresources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from humanbehavioral data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from humanbehavioral data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to an output parameter.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of networkspectrum based on information collected from human behavioral datasources and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a utilization parameter for the outputof the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of energycredits based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from humanbehavioral data sources and having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from human behavioral datasources and having an expert system that predicts a forward market pricein a market for spectrum or network bandwidth based on an understandingobtained by analyzing social data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from human behavioral data sources and having an intelligentagent that is configured to solicit the attention resources of anotherexternal intelligent agent. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from human behavioral data sources and having a machine thatautomatically purchases attention resources in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from humanbehavioral data sources and having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from human behavioral datasources and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to predict alikelihood of a facility production outcome. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from human behavioral data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from humanbehavioral data sources and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize provisioning and allocation of energy and compute resourcesto produce a favorable facility resource utilization profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from human behavioral data sources and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from human behavioral datasources and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizerequisition and provisioning of available energy and compute resourcesto produce a favorable facility input resource profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from human behavioral data sources and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from human behavioral datasources and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizeselection and configuration of an artificial intelligence system toproduce a favorable facility output profile among a set of availableartificial intelligence systems and configurations. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from human behavioral data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from human behavioral data sources and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from humanbehavioral data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of inputresources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from humanbehavioral data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from humanbehavioral data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to an output parameter.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of energycredits based on information collected from human behavioral datasources and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a utilization parameter for the outputof the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from human behavioral data sources and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market value of computecapability based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from humanbehavioral data sources and having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing social data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market value of computecapability based on information collected from human behavioral datasources and having an intelligent agent that is configured to solicitthe attention resources of another external intelligent agent. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market value of computecapability based on information collected from human behavioral datasources and having a machine that automatically purchases attentionresources in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from human behavioral data sources and having afleet of machines that automatically aggregate purchasing in a forwardmarket for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from human behavioral data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from human behavioral data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from humanbehavioral data sources and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize provisioning and allocation of energy and compute resourcesto produce a favorable facility resource utilization profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from human behavioral data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market value of computecapability based on information collected from human behavioral datasources and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizerequisition and provisioning of available energy and compute resourcesto produce a favorable facility input resource profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from human behavioral data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market value of computecapability based on information collected from human behavioral datasources and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizeselection and configuration of an artificial intelligence system toproduce a favorable facility output profile among a set of availableartificial intelligence systems and configurations. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from human behavioral data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from human behavioral data sources and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from humanbehavioral data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of inputresources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from humanbehavioral data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from humanbehavioral data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to an output parameter.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market value of computecapability based on information collected from human behavioral datasources and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a utilization parameter for the outputof the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from human behavioral data sources and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havingan expert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing social data sources andexecutes a transaction based on the forward market prediction and havingan intelligent agent that is configured to solicit the attentionresources of another external intelligent agent. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing social datasources and executes a transaction based on the forward marketprediction and having a machine that automatically purchases attentionresources in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing social datasources and executes a transaction based on the forward marketprediction and having a fleet of machines that automatically aggregatepurchasing in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing social datasources and executes a transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a likelihood of a facility production outcome. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing social datasources and executes a transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a facility production outcome. In embodiments, provided hereinis a transaction-enabling system having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing social data sources andexecutes a transaction based on the forward market prediction and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havingan expert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize provisioning and allocation of energy and compute resourcesto produce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing social data sources andexecutes a transaction based on the forward market prediction and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize requisition andprovisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing social data sources and executes a transactionbased on the forward market prediction and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing social datasources and executes a transaction based on the forward marketprediction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto generate an indication that a current or prospective customer shouldbe contacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to at least one of aninput resource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having an expert systemthat predicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing social datasources and executes a transaction based on the forward marketprediction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing social data sources and executes a transactionbased on the forward market prediction and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing social data sources andexecutes a transaction based on the forward market prediction and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a utilization parameter for the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing social data sources and executes a transactionbased on the forward market prediction and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

Referencing FIG. 62, an example transaction-enabling system 6200includes a controller 6202 having a transaction detection circuit 6204structured to interpret a transaction request value 6206, wherein thetransaction request value 6206 includes a transaction description forone of a proposed or an imminent transaction (e.g., as provided by auser, subscriber to the transaction-enabling system 6200, application orservice utilizing the transaction-enabling system 6200, etc.), andwherein the transaction description includes a cryptocurrency type value(e.g., one or more cryptocurrency types for the transaction) and atransaction amount value. The controller 6202 further includes atransaction locator circuit 6208 that determines a transaction locationparameter 6210 in response to the transaction request value 6206, wherethe transaction location parameter 6210 includes at least one of atransaction geographic value (e.g., a physical location where thetransaction will be executed, which may be adjusted, for example usingserver selections, third party transactions, or other methods) and/or atransaction jurisdiction value (e.g., a jurisdictional entity where thelaws governing the transaction will be applicable, such as a country,state, province, regional jurisdiction, and/or a governing scheme thatis inter-national or trans-national; which may be adjusted, for exampleusing server selections, third party transactions, control of thetransaction type or category invoking one of a number of jurisdictionsapplicable to a particular geographical region, etc.), and a transactionexecution circuit 6212 structured to provide a transactionimplementation command 6214 in response to the transaction locationparameter 6210.

In certain embodiments, the transaction locator circuit 6208 is furtherstructured to determine the transaction location parameter 6210 based ona tax treatment 6216 of the one of the proposed or imminent transaction,for example selecting a geographical and/or jurisdictional location froma number of available locations that will receive a favorable taxtreatment for the transaction. Certain considerations to determine afavorable tax treatment include tax laws and incentives in availablejurisdictions according to the type of transaction, specificcharacteristics of the transacting party including where income, sales,and other aspects related to the transaction will be deemed to occur andthe resulting effect on the tax treatment of the transaction, and/or theavailability of losses or other offsets to income from a transactionthat are applicable to entities involved in the transaction according tothe available jurisdictions and/or geographic regions available.

An example transaction-enabling system 6200 further includes thetransaction locator circuit 6208 further structured to select the one ofthe transaction geographic value or the transaction jurisdiction valuefrom a number of available geographic values or jurisdiction values thatprovides an improved tax treatment 6216 relative to a nominal one of thenumber of available geographic values or jurisdiction values. Forexample, an improvement to an execution of a transaction may includedetermining a nominal tax treatment 6216 (e.g., for a simplestjurisdiction such as a location of a buyer, seller, or delivery locationof a purchased item or service), and determining that an alternateavailable geography or jurisdiction is available. Improvement of a taxtreatment should be understood broadly, and can include at least one ormore of: reducing a taxable amount on a purchase; achieving a targetloss value for a transaction to offset a profit value in a particulargeography or jurisdiction for the transaction; achieving a tax paidtarget for a jurisdiction, such as to meet a minimum tax threshold asmeasured separately for an entity from the transaction, and/or to paytaxes to meet a public perception goal or tax payment target; and/orpaying taxes in a first category (e.g., sales tax) relative to a secondcategory (e.g., a short term capital gain). Accordingly, in certainembodiments, operations of the transaction locator circuit 6208 mayoperate to continuously improve or optimize tax treatment oftransactions, but may additionally or alternatively find an improved taxtreatment location without continuing to optimize a tax treatment for aparticular transaction—for example to improve the speed of execution oftransactions. In certain embodiments, operations of the transactionlocator circuit 6208 may operate to improve a tax treatment of atransaction until a threshold tax treatment value is reached—for examplean improvement amount from a nominal transaction location, and/or atarget tax treatment amount for the transaction.

An example transaction-enabling system 6200 further includes thetransaction locator circuit 6208 further structured to determining thetransaction location parameter 6210 in response to a tax treatment 6216of at least one of the cryptocurrency type value (e.g., where the typeof cryptocurrency may affect the tax treatment of the transaction—e.g.,according to a country of origin of the currency, a favored currencysuch as one issued by a government or municipality, a sanctionedcurrency that may receive unfavorable tax treatment, etc.), or a type ofthe one of the proposed or imminent transaction (e.g., where thetransaction type may lead to variable tax treatment depending upon thejurisdiction, such as a purchase of a clean energy vehicle in a countryhaving an incentive for such a purchase, etc.). A proposed or imminenttransaction, as set forth herein, can include a requested transaction(e.g., the requesting entity is attempting to make a transaction) and/ora speculative transaction (e.g., a requesting entity is trying todetermine what transaction outcomes are available for the transactionlocation, tax consequences, and/or total cost of the transaction).

An example transaction locator circuit 6208 operates an expert system6220 (and/or an AI or machine learning component) configured to usemachine learning to continuously improve the determination of thetransaction location parameter 6210 relative to a tax treatment 6216 oftransactions processed by the controller. Operations to continuouslyimprove the determination of the transaction location parameter 6210 maybe performed over a number of transactions, including transactionsrelating to the same entity or relating to a number of entities, where agiven transaction utilizes a transaction location parameter 6210 that ismade according to the state of the expert system 6220 at the time of thetransaction request value 6206, and/or wherein the expert system 6220improves an outcome for the particular transaction such as an improvedtax treatment 6216 relative to a nominal, a tax treatment 6216 that isgreater than a threshold tax treatment value, and/or a transactionlocation parameter 6210 that is determined after an optimization periodexpires (e.g., a time value threshold, which may be fixed or variable)and/or after the expert system 6220 meets convergence criteria (e.g.,further improvements appear to be lower than a threshold amount, etc.)while the transaction request value 6206 is pending. An example expertsystem 6220 is configured to aggregate regulatory information 6222 forcryptocurrency transactions from a number of jurisdictions, and tocontinuously improve the determination of the transaction locationparameter 6210 based on the aggregated regulatory information 6222. Theaggregated regulatory information 6222 may be updated, refreshed, and/oradded to over time. An example expert system 6220 utilizes machinelearning to continuously improve the determination of the transactionlocation parameter 6210 relative to secondary jurisdiction costs relatedto the cryptocurrency transaction. For example, various regulatoryschemes may affect compliance, reporting requirements, privacyconsiderations (e.g., for data), penalty schemes for particulartransaction types, export control or sanctions-related transactionconsiderations, and/or the desirability or undesirability to executetransactions that occur across jurisdictional boundaries (e.g., invokingcustoms, treaties, international legal considerations, and/orintra-country considerations such as state or provisional law versusfederal or national law). An example expert system 6220 utilizes machinelearning to continuously improve the transaction speed forcryptocurrency transactions. An example expert system 6220 utilizesmachine learning to continuously improve a favorability of contractualterms related to the cryptocurrency transaction (e.g., meetingpurchasing targets in a region, meeting transaction type targets in aregion, meeting transaction cost obligations, etc.). An example expertsystem 6220 utilizes machine learning to continuously improve acompliance of cryptocurrency transactions within the aggregatedregulatory information 6222. An example transaction-enabling system 6200further includes a transaction engine 6218 that is responsive to thetransaction implementation command 6214, for example to execute thecryptocurrency transaction in accordance with the transaction locationparameter 6210 and/or according to a cryptocurrency type provided as apart of the transaction implementation command 6214.

Referencing FIG. 63, an example procedure 6300 includes an operation6302 to interpret a cryptocurrency transaction request value, where thetransaction request value includes a transaction description for one ofa proposed or an imminent transaction, and where the transactiondescription includes a cryptocurrency type value and a transactionamount value. The example procedure 6300 includes an operation 6304 todetermine a transaction location parameter. The example procedure 6300further includes an operation 6308 to provide a transaction locationparameter in response to the transaction request value, where thetransaction location parameter includes at least one of a transactiongeographic value or a transaction jurisdiction value. In certainembodiments, the example procedure 6300 further includes an operation toprovide a transaction implementation command in response to thetransaction location parameter. In certain embodiments, an exampleprocedure 6300 includes an operation 6306 to determine a tax treatmentand/or a regulatory treatment of the transaction, and to provide 6308the transaction location parameter in response to the tax and/orregulatory treatment of the transaction.

Referencing FIG. 64, an example procedure 6400 includes an operation6402 to interpret a cryptocurrency transaction request value, where thetransaction request value includes a transaction description for one ofa proposed or an imminent transaction, and where the transactiondescription includes a cryptocurrency type value and a transactionamount value. The example procedure 6400 includes an operation 6404 todetermine a transaction location parameter, where the transactionlocation parameter includes at least one of a transaction geographicvalue or a transaction jurisdiction value, and an operation 6408 toexecute a transaction in response to the transaction location parameter.In certain embodiments, the procedure 6400 further includes an operation6406 to determine a tax and/or regulatory treatment of the transaction,and to adjust the transaction location parameter in response to the taxand/or regulatory treatment of the transaction before the operation 6408to execute the transaction.

Referencing FIG. 65, an example system 6500 includes a controller 6202having a smart wrapper 6502 that interprets a transaction request value6206 from a user, wherein the transaction request value includes atransaction description for an incoming transaction, and wherein thetransaction description includes a transaction amount value and at leastone of a cryptocurrency type value and a transaction location value. Thecontroller 6202 includes a transaction locator circuit 6208 thatdetermines a transaction location parameter 6210 in response to thetransaction request value 6206 and further in response to a plurality oftax treatment 6216 values corresponding to a plurality of transactionlocation parameters 6210, where the transaction location parameter 6210includes at least one of a transaction geographic value or a transactionjurisdiction value, and wherein the smart wrapper 6502 further directsan execution of the incoming transaction in response to the transactionlocation parameter 6210—for example by providing a transactionimplementation command 6214 to a transaction engine 6218. In certainembodiments, the smart wrapper 6502 can provide for enhanced speed oftransaction execution—for example by storing transaction locationparameter 6210 values according to transaction types, amounts, and/orrequesting entity characteristics, and thereby provide transactionimplementation commands 6214 more quickly than the expert system 6220provides updated transaction location parameters 6210, while stillproviding the benefit of the expert system 6220 operations over time astransaction location parameter 6210 values are updated. Additionally oralternatively, operations of the smart wrapper 6502 provide for aconsistent and/or selected user interface to the user for acceptingtransaction request values 6206, and/or to the transaction engine 6218for sending transaction implementation commands 6214, and allowing forchanges in the controller 6202 to remain invisible to the user and/ortransaction engine 6218.

Referencing FIG. 66, an example procedure 6600 includes an operation6602 to interpret a cryptocurrency transaction request value from auser, wherein the transaction request value includes a transactiondescription for an incoming transaction, and wherein the transactiondescription includes a transaction amount value and at least one of acryptocurrency type value and a transaction location value, an operation6604 to determine a transaction location parameter, an operation 6606 todetermine a number of tax treatment values corresponding to a number ofavailable transaction locations, and an operation 6608 to select anavailable transaction location as the transaction location parameter inresponse to the number of tax treatment values. The procedure 6600further includes an operation 6610 to execute a cryptocurrencytransaction in response to the selected available transaction locationparameter. In certain embodiments, operation 6608 includes selecting anavailable transaction location as a location having a favorable and/orimproved tax treatment, for example relative to a nominal or defaulttransaction location. Example and non-limiting transaction locationvalues for operation 6608 include: a location of a purchaser of thetransaction; a location of a seller of the transaction; a location of adelivery of a product or service of the transaction; a location of asupplier of a product or service of the transaction; a residencelocation of one of the purchaser, seller, or supplier of thetransaction; and/or a legally available location for the transaction.

Referencing FIG. 67, an example transaction-enabling system 6700includes a controller 6202 having a transaction detection circuit 6204structured to interpret a plurality of transaction request values 6206,wherein each transaction request value 6206 includes a transactiondescription for one of a proposed or an imminent transaction, andwherein the transaction description includes a cryptocurrency type valueand a transaction amount value. The example controller 6202 furtherincludes a transaction support circuit 6708 structured to interpret asupport resource description 6710 including at least one supportingresource for the plurality of transactions, a support utilizationcircuit 6712 structured to operate an expert system 6220, wherein theexpert system 6220 is configured to use machine learning to continuouslyimprove at least one execution parameter 6714 for the plurality oftransactions relative to the support resource description 6710, and atransaction execution circuit 6212 structured to command execution ofthe plurality of transactions (e.g., as transaction implementationcommands 6214) in response to the improved at least one executionparameter. In the example of FIG. 67, a support resource is depicted asthe transaction engine 6218, although a support resource may be anyresource utilized to support the transaction, including at least amining server, a transaction verification server, an accounting circuitor tax calculation circuit associated with an entity that is providingthe transaction request value 6206, and the like. In certainembodiments, the support resource description 6710 includes an energyprice, and/or may further include an energy resource consumption tosupport the transaction or a group of transactions. An example energyprice description includes of a of a forward price prediction, a spotprice, and/or a forward market price for the energy source. An exampleenergy price description includes one or more energy source selectionsavailable to power the execution of the transaction or a group oftransactions. An example support resource description 6710 includes astate of charge for an energy source available to power the execution ofthe transaction or a group of transactions, and/or a charge cost cycledescription for an energy storage source available to power theexecution of the transaction or a group of transactions. An examplesystem 6700 includes an energy storage source having a battery as asupporting device, and where the expert system 6220 is furtherconfigured to use machine learning to improve one or more of: a batteryenergy transfer efficiency value, a battery life value, and a batterylifetime utilization cost value.

Referencing FIG. 68, an example procedure 6800 includes an operation6802 to interpret a number of transaction request values, where eachtransaction request value includes a transaction description for one ofa proposed or an imminent transaction, and wherein the transactiondescription includes a cryptocurrency type value and a transactionamount value. The example procedure 6800 further includes an operation6804 to interpret a support resource description including at least onesupporting resource for the number of transactions, and an operation6806 to operate an expert system, where the expert system is configuredto use machine learning to continuously improve at least one executionparameter for the number of transactions relative to the supportresource description. The example procedure 6800 further includes anoperation 6808 to command execution of the number of transactions inresponse to the improved execution parameter(s).

In embodiments, provided herein is a transaction-enabling system havinga smart wrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location. In embodiments, provided herein is atransaction-enabling system having a smart wrapper for a cryptocurrencycoin that directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location and having aself-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment. In embodiments, provided herein is a transaction-enablingsystem having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location and having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status. In embodiments, provided herein is a transaction-enablingsystem having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location and having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information. In embodiments, provided herein is atransaction-enabling system having a smart wrapper for a cryptocurrencycoin that directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location and having an expertsystem that uses machine learning to optimize the execution of acryptocurrency transaction based on real time energy price informationfor an available energy source. In embodiments, provided herein is atransaction-enabling system having a smart wrapper for a cryptocurrencycoin that directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location and having an expertsystem that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location and having an expert system that uses machinelearning to optimize charging and recharging cycle of a rechargeablebattery system to provide energy for execution of a cryptocurrencytransaction. In embodiments, provided herein is a transaction-enablingsystem having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a smart wrapper for acryptocurrency coin that directs execution of a transaction involvingthe coin to a geographic location based on tax treatment of at least oneof the coin and the transaction in the geographic location and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a smart wrapperfor a cryptocurrency coin that directs execution of a transactioninvolving the coin to a geographic location based on tax treatment of atleast one of the coin and the transaction in the geographic location andhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing social network datasources and executes a cryptocurrency transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a smart wrapper for a cryptocurrencycoin that directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location and having an expertsystem that predicts a forward market price in an energy market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location and having an expert system that predicts a forwardmarket price in an energy market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having a smart wrapper for a cryptocurrencycoin that directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location and having an expertsystem that predicts a forward market price in a market for computingresources based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a smart wrapper for a cryptocurrencycoin that directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location and having an expertsystem that predicts a forward market price in a market for spectrum ornetwork bandwidth based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a smart wrapper for a cryptocurrencycoin that directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location and having an expertsystem that predicts a forward market price in a market for computingresources based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location and having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location and having an expert system that predicts a forwardmarket price in a market for advertising based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a smart wrapperfor a cryptocurrency coin that directs execution of a transactioninvolving the coin to a geographic location based on tax treatment of atleast one of the coin and the transaction in the geographic location andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a smart wrapper for a cryptocurrencycoin that directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga smart wrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom automated agent behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a smart wrapper for acryptocurrency coin that directs execution of a transaction involvingthe coin to a geographic location based on tax treatment of at least oneof the coin and the transaction in the geographic location and having amachine that automatically forecasts forward market value of computecapability based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a smart wrapper for a cryptocurrencycoin that directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a smart wrapperfor a cryptocurrency coin that directs execution of a transactioninvolving the coin to a geographic location based on tax treatment of atleast one of the coin and the transaction in the geographic location andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a smart wrapper for a cryptocurrencycoin that directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga smart wrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location and having a machine that automatically forecastsforward market pricing of energy prices based on information collectedfrom human behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a smart wrapper for a cryptocurrencycoin that directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom human behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a smart wrapper for a cryptocurrencycoin that directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location and having an expert system that predicts a forwardmarket price in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing social data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a smart wrapperfor a cryptocurrency coin that directs execution of a transactioninvolving the coin to a geographic location based on tax treatment of atleast one of the coin and the transaction in the geographic location andhaving an intelligent agent that is configured to solicit the attentionresources of another external intelligent agent. In embodiments,provided herein is a transaction-enabling system having a smart wrapperfor a cryptocurrency coin that directs execution of a transactioninvolving the coin to a geographic location based on tax treatment of atleast one of the coin and the transaction in the geographic location andhaving a machine that automatically purchases attention resources in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a smart wrapper for a cryptocurrencycoin that directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location and having a fleet ofmachines that automatically aggregate purchasing in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a likelihood of a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a facility production outcome. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a smart wrapper for a cryptocurrencycoin that directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having a smart wrapper for a cryptocurrencycoin that directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to at least one of aninput resource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a smart wrapperfor a cryptocurrency coin that directs execution of a transactioninvolving the coin to a geographic location based on tax treatment of atleast one of the coin and the transaction in the geographic location andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of input resources. In embodiments,provided herein is a transaction-enabling system having a smart wrapperfor a cryptocurrency coin that directs execution of a transactioninvolving the coin to a geographic location based on tax treatment of atleast one of the coin and the transaction in the geographic location andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of facility resources. In embodiments,provided herein is a transaction-enabling system having a smart wrapperfor a cryptocurrency coin that directs execution of a transactioninvolving the coin to a geographic location based on tax treatment of atleast one of the coin and the transaction in the geographic location andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter. In embodiments, providedherein is a transaction-enabling system having a smart wrapper for acryptocurrency coin that directs execution of a transaction involvingthe coin to a geographic location based on tax treatment of at least oneof the coin and the transaction in the geographic location and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility. Inembodiments, provided herein is a transaction-enabling system having asmart wrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of parameters received from a digital twin for thefacility.

In embodiments, provided herein is a transaction-enabling system havinga self-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment. In embodiments, provided herein is a transaction-enablingsystem having a self-executing cryptocurrency coin that commits atransaction upon recognizing a location-based parameter that providesfavorable tax treatment and having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status. In embodiments, provided herein is a transaction-enablingsystem having a self-executing cryptocurrency coin that commits atransaction upon recognizing a location-based parameter that providesfavorable tax treatment and having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information. In embodiments, provided herein is atransaction-enabling system having a self-executing cryptocurrency cointhat commits a transaction upon recognizing a location-based parameterthat provides favorable tax treatment and having an expert system thatuses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source. In embodiments, provided herein is a transaction-enablingsystem having a self-executing cryptocurrency coin that commits atransaction upon recognizing a location-based parameter that providesfavorable tax treatment and having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon an understanding of available energy sources to power computingresources to execute the transaction. In embodiments, provided herein isa transaction-enabling system having a self-executing cryptocurrencycoin that commits a transaction upon recognizing a location-basedparameter that provides favorable tax treatment and having an expertsystem that uses machine learning to optimize charging and rechargingcycle of a rechargeable battery system to provide energy for executionof a cryptocurrency transaction. In embodiments, provided herein is atransaction-enabling system having a self-executing cryptocurrency cointhat commits a transaction upon recognizing a location-based parameterthat provides favorable tax treatment and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment andhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a self-executing cryptocurrency coin that commits atransaction upon recognizing a location-based parameter that providesfavorable tax treatment and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment andhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing social network datasources and executes a cryptocurrency transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a self-executing cryptocurrency cointhat commits a transaction upon recognizing a location-based parameterthat provides favorable tax treatment and having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having aself-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment and having an expert system that predicts a forward marketprice in an energy market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having a self-executing cryptocurrency cointhat commits a transaction upon recognizing a location-based parameterthat provides favorable tax treatment and having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a self-executing cryptocurrency coin that commits atransaction upon recognizing a location-based parameter that providesfavorable tax treatment and having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a self-executing cryptocurrency coin that commits atransaction upon recognizing a location-based parameter that providesfavorable tax treatment and having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having aself-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment and having an expert system that predicts a forward marketprice in a market for advertising based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a self-executing cryptocurrencycoin that commits a transaction upon recognizing a location-basedparameter that provides favorable tax treatment and having an expertsystem that predicts a forward market price in a market for advertisingbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a self-executing cryptocurrency coin that commits atransaction upon recognizing a location-based parameter that providesfavorable tax treatment and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a self-executing cryptocurrency cointhat commits a transaction upon recognizing a location-based parameterthat provides favorable tax treatment and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga self-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a self-executing cryptocurrencycoin that commits a transaction upon recognizing a location-basedparameter that provides favorable tax treatment and having a machinethat automatically forecasts forward market pricing of energy pricesbased on information collected from business entity behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a self-executing cryptocurrency coin that commits atransaction upon recognizing a location-based parameter that providesfavorable tax treatment and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having aself-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected frombusiness entity behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a self-executing cryptocurrencycoin that commits a transaction upon recognizing a location-basedparameter that provides favorable tax treatment and having a machinethat automatically forecasts forward market value of compute capabilitybased on information collected from business entity behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a self-executing cryptocurrency coin that commits atransaction upon recognizing a location-based parameter that providesfavorable tax treatment and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a self-executing cryptocurrency coin that commits atransaction upon recognizing a location-based parameter that providesfavorable tax treatment and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a self-executing cryptocurrency coin that commits atransaction upon recognizing a location-based parameter that providesfavorable tax treatment and having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing social data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having aself-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment and having an intelligent agent that is configured to solicitthe attention resources of another external intelligent agent. Inembodiments, provided herein is a transaction-enabling system having aself-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment and having a machine that automatically purchases attentionresources in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment andhaving a fleet of machines that automatically aggregate purchasing in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a self-executing cryptocurrency cointhat commits a transaction upon recognizing a location-based parameterthat provides favorable tax treatment and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a likelihood of a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a self-executing cryptocurrency coin that commits atransaction upon recognizing a location-based parameter that providesfavorable tax treatment and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto predict a facility production outcome. In embodiments, providedherein is a transaction-enabling system having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga self-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizeprovisioning and allocation of energy and compute resources to produce afavorable facility resource output selection among a set of availableoutputs. In embodiments, provided herein is a transaction-enablingsystem having a self-executing cryptocurrency coin that commits atransaction upon recognizing a location-based parameter that providesfavorable tax treatment and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having a self-executing cryptocurrency cointhat commits a transaction upon recognizing a location-based parameterthat provides favorable tax treatment and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize selectionand configuration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a self-executing cryptocurrencycoin that commits a transaction upon recognizing a location-basedparameter that provides favorable tax treatment and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a self-executing cryptocurrency cointhat commits a transaction upon recognizing a location-based parameterthat provides favorable tax treatment and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a self-executing cryptocurrency coin that commits atransaction upon recognizing a location-based parameter that providesfavorable tax treatment and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of inputresources. In embodiments, provided herein is a transaction-enablingsystem having a self-executing cryptocurrency coin that commits atransaction upon recognizing a location-based parameter that providesfavorable tax treatment and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a self-executing cryptocurrency coin that commits atransaction upon recognizing a location-based parameter that providesfavorable tax treatment and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to an output parameter.In embodiments, provided herein is a transaction-enabling system havinga self-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having a self-executing cryptocurrency cointhat commits a transaction upon recognizing a location-based parameterthat provides favorable tax treatment and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havingan expert system that uses machine learning to optimize the execution ofcryptocurrency transactions based on tax status. In embodiments,provided herein is a transaction-enabling system having an expert systemthat uses machine learning to optimize the execution of cryptocurrencytransactions based on tax status and having an expert system thataggregates regulatory information covering cryptocurrency transactionsand automatically selects a jurisdiction for an operation based on theregulatory information. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status and having an expert system that uses machine learning tooptimize the execution of a cryptocurrency transaction based on realtime energy price information for an available energy source. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution ofcryptocurrency transactions based on tax status and having an expertsystem that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution ofcryptocurrency transactions based on tax status and having an expertsystem that uses machine learning to optimize charging and rechargingcycle of a rechargeable battery system to provide energy for executionof a cryptocurrency transaction. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status and having an expert system that predicts a forward marketprice in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status and having an expert system that predicts a forward marketprice in a market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status and having an expert system that predicts a forward marketprice in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having an expert systemthat uses machine learning to optimize the execution of cryptocurrencytransactions based on tax status and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing social network data sources and executes acryptocurrency transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution ofcryptocurrency transactions based on tax status and having an expertsystem that predicts a forward market price in an energy market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution ofcryptocurrency transactions based on tax status and having an expertsystem that predicts a forward market price in an energy market based onan understanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution ofcryptocurrency transactions based on tax status and having an expertsystem that predicts a forward market price in a market for computingresources based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status and having an expert system that predicts a forward marketprice in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution ofcryptocurrency transactions based on tax status and having an expertsystem that predicts a forward market price in a market for computingresources based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having an expert system that uses machine learning to optimizethe execution of cryptocurrency transactions based on tax status andhaving an expert system that predicts a forward market price in a marketfor advertising based on an understanding obtained by analyzing Internetof Things data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status and having an expert system that predicts a forward marketprice in a market for advertising based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status and having a machine that automatically forecasts forwardmarket pricing of energy prices based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having an expert system that usesmachine learning to optimize the execution of cryptocurrencytransactions based on tax status and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution ofcryptocurrency transactions based on tax status and having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from automated agent behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that uses machine learning to optimizethe execution of cryptocurrency transactions based on tax status andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status and having a machine that automatically forecasts forwardmarket pricing of energy prices based on information collected frombusiness entity behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having an expert system that usesmachine learning to optimize the execution of cryptocurrencytransactions based on tax status and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution ofcryptocurrency transactions based on tax status and having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from business entity behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that uses machine learning to optimizethe execution of cryptocurrency transactions based on tax status andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status and having a machine that automatically forecasts forwardmarket pricing of energy prices based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status and having an expert system that predicts a forward marketprice in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing social data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having an expert systemthat uses machine learning to optimize the execution of cryptocurrencytransactions based on tax status and having an intelligent agent that isconfigured to solicit the attention resources of another externalintelligent agent. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status and having a machine that automatically purchasesattention resources in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having an expert systemthat uses machine learning to optimize the execution of cryptocurrencytransactions based on tax status and having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution ofcryptocurrency transactions based on tax status and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a facility production outcome. In embodiments, provided hereinis a transaction-enabling system having an expert system that usesmachine learning to optimize the execution of cryptocurrencytransactions based on tax status and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource utilizationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having an expert system thatuses machine learning to optimize the execution of cryptocurrencytransactions based on tax status and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource outputselection among a set of available outputs. In embodiments, providedherein is a transaction-enabling system having an expert system thatuses machine learning to optimize the execution of cryptocurrencytransactions based on tax status and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize requisition and provisioning of availableenergy and compute resources to produce a favorable facility inputresource profile among a set of available profiles. In embodiments,provided herein is a transaction-enabling system having an expert systemthat uses machine learning to optimize the execution of cryptocurrencytransactions based on tax status and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having an expert system thatuses machine learning to optimize the execution of cryptocurrencytransactions based on tax status and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize selection and configuration of an artificialintelligence system to produce a favorable facility output profile amonga set of available artificial intelligence systems and configurations.In embodiments, provided herein is a transaction-enabling system havingan expert system that uses machine learning to optimize the execution ofcryptocurrency transactions based on tax status and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having an expert systemthat uses machine learning to optimize the execution of cryptocurrencytransactions based on tax status and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to a setof input resources. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution ofcryptocurrency transactions based on tax status and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of parameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havingan expert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information. In embodiments,provided herein is a transaction-enabling system having an expert systemthat aggregates regulatory information covering cryptocurrencytransactions and automatically selects a jurisdiction for an operationbased on the regulatory information and having an expert system thatuses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source. In embodiments, provided herein is a transaction-enablingsystem having an expert system that aggregates regulatory informationcovering cryptocurrency transactions and automatically selects ajurisdiction for an operation based on the regulatory information andhaving an expert system that uses machine learning to optimize theexecution of a cryptocurrency transaction based on an understanding ofavailable energy sources to power computing resources to execute thetransaction. In embodiments, provided herein is a transaction-enablingsystem having an expert system that aggregates regulatory informationcovering cryptocurrency transactions and automatically selects ajurisdiction for an operation based on the regulatory information andhaving an expert system that uses machine learning to optimize chargingand recharging cycle of a rechargeable battery system to provide energyfor execution of a cryptocurrency transaction. In embodiments, providedherein is a transaction-enabling system having an expert system thataggregates regulatory information covering cryptocurrency transactionsand automatically selects a jurisdiction for an operation based on theregulatory information and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having an expert systemthat aggregates regulatory information covering cryptocurrencytransactions and automatically selects a jurisdiction for an operationbased on the regulatory information and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing social network data sources and executes acryptocurrency transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information and having an expertsystem that predicts a forward market price in an energy market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information and having an expertsystem that predicts a forward market price in an energy market based onan understanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information and having an expertsystem that predicts a forward market price in a market for computingresources based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information and having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having an expert system that aggregates regulatory informationcovering cryptocurrency transactions and automatically selects ajurisdiction for an operation based on the regulatory information andhaving an expert system that predicts a forward market price in a marketfor computing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information and having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information and having an expertsystem that predicts a forward market price in a market for advertisingbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having an expert system that aggregates regulatory informationcovering cryptocurrency transactions and automatically selects ajurisdiction for an operation based on the regulatory information andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat aggregates regulatory information covering cryptocurrencytransactions and automatically selects a jurisdiction for an operationbased on the regulatory information and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havingan expert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information and having a machinethat automatically forecasts forward market value of compute capabilitybased on information collected from automated agent behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that aggregates regulatory informationcovering cryptocurrency transactions and automatically selects ajurisdiction for an operation based on the regulatory information andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat aggregates regulatory information covering cryptocurrencytransactions and automatically selects a jurisdiction for an operationbased on the regulatory information and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havingan expert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information and having a machinethat automatically forecasts forward market value of compute capabilitybased on information collected from business entity behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that aggregates regulatory informationcovering cryptocurrency transactions and automatically selects ajurisdiction for an operation based on the regulatory information andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that aggregates regulatory informationcovering cryptocurrency transactions and automatically selects ajurisdiction for an operation based on the regulatory information andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that aggregates regulatory informationcovering cryptocurrency transactions and automatically selects ajurisdiction for an operation based on the regulatory information andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that aggregates regulatory informationcovering cryptocurrency transactions and automatically selects ajurisdiction for an operation based on the regulatory information andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that aggregates regulatory informationcovering cryptocurrency transactions and automatically selects ajurisdiction for an operation based on the regulatory information andhaving an expert system that predicts a forward market price in a marketfor spectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information and having an intelligent agent that isconfigured to solicit the attention resources of another externalintelligent agent. In embodiments, provided herein is atransaction-enabling system having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information and having a machine that automatically purchasesattention resources in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having an expert systemthat aggregates regulatory information covering cryptocurrencytransactions and automatically selects a jurisdiction for an operationbased on the regulatory information and having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having anexpert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto predict a facility production outcome. In embodiments, providedherein is a transaction-enabling system having an expert system thataggregates regulatory information covering cryptocurrency transactionsand automatically selects a jurisdiction for an operation based on theregulatory information and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize provisioning and allocation of energy and compute resourcesto produce a favorable facility resource utilization profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize provisioning and allocation of energy and compute resourcesto produce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having anexpert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to at least one of aninput resource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having an expert systemthat aggregates regulatory information covering cryptocurrencytransactions and automatically selects a jurisdiction for an operationbased on the regulatory information and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to a setof input resources. In embodiments, provided herein is atransaction-enabling system having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that aggregates regulatory informationcovering cryptocurrency transactions and automatically selects ajurisdiction for an operation based on the regulatory information andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter. In embodiments, providedherein is a transaction-enabling system having an expert system thataggregates regulatory information covering cryptocurrency transactionsand automatically selects a jurisdiction for an operation based on theregulatory information and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a utilizationparameter for the output of the facility. In embodiments, providedherein is a transaction-enabling system having an expert system thataggregates regulatory information covering cryptocurrency transactionsand automatically selects a jurisdiction for an operation based on theregulatory information and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of parameters received from a digital twin for thefacility.

In embodiments, provided herein is a transaction-enabling system havingan expert system that uses machine learning to optimize the execution ofa cryptocurrency transaction based on real time energy price informationfor an available energy source. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon real time energy price information for an available energy source andhaving an expert system that uses machine learning to optimize theexecution of a cryptocurrency transaction based on an understanding ofavailable energy sources to power computing resources to execute thetransaction. In embodiments, provided herein is a transaction-enablingsystem having an expert system that uses machine learning to optimizethe execution of a cryptocurrency transaction based on real time energyprice information for an available energy source and having an expertsystem that uses machine learning to optimize charging and rechargingcycle of a rechargeable battery system to provide energy for executionof a cryptocurrency transaction. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon real time energy price information for an available energy source andhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having an expert system that uses machine learning to optimizethe execution of a cryptocurrency transaction based on real time energyprice information for an available energy source and having an expertsystem that predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on real time energy price informationfor an available energy source and having an expert system that predictsa forward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having an expert systemthat uses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source and having an expert system that predicts a forward marketprice in a market based on an understanding obtained by analyzing socialnetwork data sources and executes a cryptocurrency transaction based onthe forward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon real time energy price information for an available energy source andhaving an expert system that predicts a forward market price in anenergy market based on an understanding obtained by analyzing Internetof Things data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon real time energy price information for an available energy source andhaving an expert system that predicts a forward market price in anenergy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon real time energy price information for an available energy source andhaving an expert system that predicts a forward market price in a marketfor computing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon real time energy price information for an available energy source andhaving an expert system that predicts a forward market price in a marketfor spectrum or network bandwidth based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having an expert system that usesmachine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source and having an expert system that predicts a forward marketprice in a market for computing resources based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having an expert systemthat uses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source and having an expert system that predicts a forward marketprice in a market for advertising based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having an expert system that usesmachine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source and having an expert system that predicts a forward marketprice in a market for advertising based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon real time energy price information for an available energy source andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon real time energy price information for an available energy source andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon real time energy price information for an available energy source andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon real time energy price information for an available energy source andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon real time energy price information for an available energy source andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon real time energy price information for an available energy source andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon real time energy price information for an available energy source andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon real time energy price information for an available energy source andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon real time energy price information for an available energy source andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that uses machine learning to optimizethe execution of a cryptocurrency transaction based on real time energyprice information for an available energy source and having a machinethat automatically forecasts forward market pricing of network spectrumbased on information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on real time energy price informationfor an available energy source and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having an expert system thatuses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon real time energy price information for an available energy source andhaving an expert system that predicts a forward market price in a marketfor spectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon real time energy price information for an available energy source andhaving an intelligent agent that is configured to solicit the attentionresources of another external intelligent agent. In embodiments,provided herein is a transaction-enabling system having an expert systemthat uses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source and having a machine that automatically purchasesattention resources in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having an expert systemthat uses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source and having a fleet of machines that automaticallyaggregate purchasing in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having an expert systemthat uses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a likelihood of a facility production outcome. In embodiments,provided herein is a transaction-enabling system having an expert systemthat uses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a facility production outcome. In embodiments, provided hereinis a transaction-enabling system having an expert system that usesmachine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon real time energy price information for an available energy source andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on real time energy price informationfor an available energy source and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize requisition and provisioning of availableenergy and compute resources to produce a favorable facility inputresource profile among a set of available profiles. In embodiments,provided herein is a transaction-enabling system having an expert systemthat uses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon real time energy price information for an available energy source andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize selectionand configuration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having an expert system that usesmachine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on real time energy price informationfor an available energy source and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to atleast one of an input resource, a facility resource, an output parameterand an external condition related to the output of the facility. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on real time energy price informationfor an available energy source and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to a setof input resources. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon real time energy price information for an available energy source andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of facility resources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat uses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to an output parameter. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on real time energy price informationfor an available energy source and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to autilization parameter for the output of the facility. In embodiments,provided herein is a transaction-enabling system having an expert systemthat uses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of parameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havingan expert system that uses machine learning to optimize the execution ofa cryptocurrency transaction based on an understanding of availableenergy sources to power computing resources to execute the transaction.In embodiments, provided herein is a transaction-enabling system havingan expert system that uses machine learning to optimize the execution ofa cryptocurrency transaction based on an understanding of availableenergy sources to power computing resources to execute the transactionand having an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction andhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having an expert system that uses machine learning to optimizethe execution of a cryptocurrency transaction based on an understandingof available energy sources to power computing resources to execute thetransaction and having an expert system that predicts a forward marketprice in a market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon an understanding of available energy sources to power computingresources to execute the transaction and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction andhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing social network datasources and executes a cryptocurrency transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon an understanding of available energy sources to power computingresources to execute the transaction and having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction andhaving an expert system that predicts a forward market price in anenergy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon an understanding of available energy sources to power computingresources to execute the transaction and having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having an expert system that uses machine learning to optimizethe execution of a cryptocurrency transaction based on an understandingof available energy sources to power computing resources to execute thetransaction and having an expert system that predicts a forward marketprice in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction andhaving an expert system that predicts a forward market price in a marketfor computing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon an understanding of available energy sources to power computingresources to execute the transaction and having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction andhaving an expert system that predicts a forward market price in a marketfor advertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon an understanding of available energy sources to power computingresources to execute the transaction and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon an understanding of available energy sources to power computingresources to execute the transaction and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havingan expert system that uses machine learning to optimize the execution ofa cryptocurrency transaction based on an understanding of availableenergy sources to power computing resources to execute the transactionand having a machine that automatically forecasts forward market valueof compute capability based on information collected from automatedagent behavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon an understanding of available energy sources to power computingresources to execute the transaction and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon an understanding of available energy sources to power computingresources to execute the transaction and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havingan expert system that uses machine learning to optimize the execution ofa cryptocurrency transaction based on an understanding of availableenergy sources to power computing resources to execute the transactionand having a machine that automatically forecasts forward market valueof compute capability based on information collected from businessentity behavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon an understanding of available energy sources to power computingresources to execute the transaction and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having an expert system that uses machine learning to optimizethe execution of a cryptocurrency transaction based on an understandingof available energy sources to power computing resources to execute thetransaction and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon an understanding of available energy sources to power computingresources to execute the transaction and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction andhaving an expert system that predicts a forward market price in a marketfor spectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon an understanding of available energy sources to power computingresources to execute the transaction and having an intelligent agentthat is configured to solicit the attention resources of anotherexternal intelligent agent. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon an understanding of available energy sources to power computingresources to execute the transaction and having a machine thatautomatically purchases attention resources in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having an expert system that uses machine learning to optimizethe execution of a cryptocurrency transaction based on an understandingof available energy sources to power computing resources to execute thetransaction and having a fleet of machines that automatically aggregatepurchasing in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having an expert system thatuses machine learning to optimize the execution of a cryptocurrencytransaction based on an understanding of available energy sources topower computing resources to execute the transaction and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon an understanding of available energy sources to power computingresources to execute the transaction and having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a facility production outcome. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havingan expert system that uses machine learning to optimize the execution ofa cryptocurrency transaction based on an understanding of availableenergy sources to power computing resources to execute the transactionand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize requisitionand provisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having an expert system that uses machine learning to optimizethe execution of a cryptocurrency transaction based on an understandingof available energy sources to power computing resources to execute thetransaction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon an understanding of available energy sources to power computingresources to execute the transaction and having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize selection and configuration of an artificialintelligence system to produce a favorable facility output profile amonga set of available artificial intelligence systems and configurations.In embodiments, provided herein is a transaction-enabling system havingan expert system that uses machine learning to optimize the execution ofa cryptocurrency transaction based on an understanding of availableenergy sources to power computing resources to execute the transactionand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility. In embodiments,provided herein is a transaction-enabling system having an expert systemthat uses machine learning to optimize the execution of a cryptocurrencytransaction based on an understanding of available energy sources topower computing resources to execute the transaction and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having an expert system that uses machine learning to optimizethe execution of a cryptocurrency transaction based on an understandingof available energy sources to power computing resources to execute thetransaction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of facility resources. In embodiments,provided herein is a transaction-enabling system having an expert systemthat uses machine learning to optimize the execution of a cryptocurrencytransaction based on an understanding of available energy sources topower computing resources to execute the transaction and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon an understanding of available energy sources to power computingresources to execute the transaction and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to autilization parameter for the output of the facility. In embodiments,provided herein is a transaction-enabling system having an expert systemthat uses machine learning to optimize the execution of a cryptocurrencytransaction based on an understanding of available energy sources topower computing resources to execute the transaction and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havingan expert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction. In embodiments, providedherein is a transaction-enabling system having an expert system thatuses machine learning to optimize charging and recharging cycle of arechargeable battery system to provide energy for execution of acryptocurrency transaction and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having an expert system that usesmachine learning to optimize charging and recharging cycle of arechargeable battery system to provide energy for execution of acryptocurrency transaction and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize charging and recharging cycle of a rechargeablebattery system to provide energy for execution of a cryptocurrencytransaction and having an expert system that predicts a forward marketprice in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having an expert systemthat uses machine learning to optimize charging and recharging cycle ofa rechargeable battery system to provide energy for execution of acryptocurrency transaction and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having an expert systemthat uses machine learning to optimize charging and recharging cycle ofa rechargeable battery system to provide energy for execution of acryptocurrency transaction and having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having an expert systemthat uses machine learning to optimize charging and recharging cycle ofa rechargeable battery system to provide energy for execution of acryptocurrency transaction and having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having an expert systemthat uses machine learning to optimize charging and recharging cycle ofa rechargeable battery system to provide energy for execution of acryptocurrency transaction and having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction and having an expert systemthat predicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize charging and recharging cycle of a rechargeablebattery system to provide energy for execution of a cryptocurrencytransaction and having an expert system that predicts a forward marketprice in a market for computing resources based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having an expert systemthat uses machine learning to optimize charging and recharging cycle ofa rechargeable battery system to provide energy for execution of acryptocurrency transaction and having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction and having an expert systemthat predicts a forward market price in a market for advertising basedon an understanding obtained by analyzing social network data sourcesand executes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havingan expert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havingan expert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havingan expert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havingan expert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havingan expert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havingan expert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction and having an expert systemthat predicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing social datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction and havingan intelligent agent that is configured to solicit the attentionresources of another external intelligent agent. In embodiments,provided herein is a transaction-enabling system having an expert systemthat uses machine learning to optimize charging and recharging cycle ofa rechargeable battery system to provide energy for execution of acryptocurrency transaction and having a machine that automaticallypurchases attention resources in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction and having a fleet of machinesthat automatically aggregate purchasing in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize charging and recharging cycle of a rechargeablebattery system to provide energy for execution of a cryptocurrencytransaction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a facility production outcome. In embodiments, provided hereinis a transaction-enabling system having an expert system that usesmachine learning to optimize charging and recharging cycle of arechargeable battery system to provide energy for execution of acryptocurrency transaction and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource utilizationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having an expert system thatuses machine learning to optimize charging and recharging cycle of arechargeable battery system to provide energy for execution of acryptocurrency transaction and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource outputselection among a set of available outputs. In embodiments, providedherein is a transaction-enabling system having an expert system thatuses machine learning to optimize charging and recharging cycle of arechargeable battery system to provide energy for execution of acryptocurrency transaction and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize requisition and provisioning of availableenergy and compute resources to produce a favorable facility inputresource profile among a set of available profiles. In embodiments,provided herein is a transaction-enabling system having an expert systemthat uses machine learning to optimize charging and recharging cycle ofa rechargeable battery system to provide energy for execution of acryptocurrency transaction and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles.

In embodiments, provided herein is a transaction-enabling system havingan expert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having an expert system that usesmachine learning to optimize charging and recharging cycle of arechargeable battery system to provide energy for execution of acryptocurrency transaction and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to generate an indication that a current or prospectivecustomer should be contacted about an output that can be provided by thefacility. In embodiments, provided herein is a transaction-enablingsystem having an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to at least one of an input resource, a facilityresource, an output parameter and an external condition related to theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having an expert system that uses machinelearning to optimize charging and recharging cycle of a rechargeablebattery system to provide energy for execution of a cryptocurrencytransaction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of facility resources. In embodiments, provided hereinis a transaction-enabling system having an expert system that usesmachine learning to optimize charging and recharging cycle of arechargeable battery system to provide energy for execution of acryptocurrency transaction and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to an output parameter.In embodiments, provided herein is a transaction-enabling system havingan expert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility. Inembodiments, provided herein is a transaction-enabling system having anexpert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

Referencing FIG. 69, an example transaction-enabling system 6900includes a controller 6902 having: an attention market access circuit6904 structured to interpret a number of attention-related resources6906 available on an attention related resource market 6907, anintelligent agent circuit 6908 that determines an attention-relatedresource acquisition value 6910 based on a cost parameter 6916 of atleast one of the number of attention-related resources 6906, and anattention acquisition circuit 6912 structured to solicit anattention-related resource 6906 (e.g., by providing an attention-relatedresource solicitation 6914 to the attention-related resource market6907) in response to the attention-related resource acquisition value6910. In certain embodiments, an attention-related resource 6906 may beavailable for purchase or sale on the attention-related resource market6907, and/or in certain embodiments an attention-related resource 6906may be provided by and/or accessible to the controller 6902 for sale onthe attention-related resource market 6907.

An example system 6900 includes where the attention acquisition circuit6912 is further structured to solicit the attention-related resource6906 by performing an operation such as: purchasing theattention-related resource 6906 from the attention related resourcemarket 6907; selling the attention-related resource 6906 to theattention related resource market 6907; making an offer to sell theattention-related resource 6906 to a second intelligent agent (notshown); and/or making an offer to purchase the attention-relatedresource from the second intelligent agent. An example system 6900includes where the number of attention-related resources 6906 includeresources such as: an advertising placement; a search listing; a keywordlisting; a banner advertisement; a video advertisement; an embeddedvideo advertisement; a panel activity participation; a survey activityparticipation; a trial activity participation; and/or a pilot activityplacement or participation.

An example system 6900 includes one or more of: where the attentionrelated resource market 6907 includes a spot market for at least one ofthe number of attention-related resources 6906; where the cost parameter6916 of at least one of the number of attention-related resources 6906includes a future predicted cost of the at least one of the number ofattention-related resources, and where the intelligent agent circuit6908 is further structured to determine the attention-related resourceacquisition value 6910 in response to a comparison of a first cost onthe spot market with the cost parameter 6916; where the attentionrelated resource market 6907 includes a forward market for at least oneof the number of attention-related resources 6906, and where the costparameter 6916 of the at least one of the number of attention-relatedresources 6906 includes a predicted future cost; and/or where the costparameter 6916 of at least one of the number of attention-relatedresources 6906 includes a future predicted cost of the at least one ofthe number of attention-related resources 6906, and where theintelligent agent circuit 6908 is further structured to determine theattention-related resource acquisition value 6910 in response to acomparison of a first cost on the forward market with the cost parameter6916. An example system includes the intelligent agent circuit 6908further structured to determine the attention-related resourceacquisition value 6910 in response to the cost parameter 6916 of the atleast one of the number of attention-related resources having a valuethat is outside of an expected cost range for the at least one of thenumber of attention-related resources 6906. An example system includesthe intelligent agent circuit 6908 is further structured to determinethe attention-related resource acquisition value 6910 in response to afunction of: the cost parameter 6916 of the at least one of the numberof attention-related resources, and/or an effectiveness parameter 6918of the at least one of the number of attention-related resources 6906.In certain further embodiments, an example controller 6902 furtherincludes an external data circuit 6920 structured to interpret a socialmedia data source 6922, and where the intelligent agent circuit 6908 isfurther structured to determine, in response to the social media datasource 6922, at least one of a future predicted cost of the at least oneof the number of attention-related resources 6906, and to utilize thefuture predicted cost as the cost parameter 6916 and/or theeffectiveness parameter 6918 of the at least one of the number ofattention-related resources 6906.

Referencing FIG. 71, an example system 7100 includes a fleet ofmachines, where each machine includes a task system having a core taskand further at least one of a compute task or a network task (e.g., taskmachines 7102). The system 7100 includes a controller 6902 having: anattention market access circuit 6904 structured to interpret a number ofattention-related resources 6906 available on an attention relatedresource market 6907; an intelligent agent circuit 6908 structured todetermine a number of attention-related resource acquisition value 6910based on a cost parameter 6916 of at least one of the number ofattention-related resources 6906, and further based on the core task forthe corresponding machine of the fleet of machines 7102. The examplesystem 7100 further includes an attention purchase aggregating circuit7104 structured to determine an aggregate attention-related resourcepurchase value 7106 in response to the number of attention-relatedresource acquisition values 6910 from the intelligent agent circuitcorresponding to each machine of the fleet of the machines 7102; and anattention acquisition circuit 7108 structured to purchase anattention-related resource 6906 in response to the aggregateattention-related resource purchase value 7106.

An example system 7100 includes where the attention purchase aggregatingcircuit 7104 is positioned at a location selected from the locationsconsisting of: at least partially distributed on a number of thecontrollers corresponding to machines of the fleet of machines 7102; ona selected controller corresponding to one of the machines of the fleetof machines 7102; and on a system controller 6902 communicativelycoupled to the number of the controllers corresponding to machines ofthe fleet of machines 7102 (e.g., consistent with the depiction of FIG.71). An example system 7100 includes where the attention acquisitioncircuit 7108 is positioned at a location selected from the locationsconsisting of: at least partially distributed on a number of thecontrollers corresponding to machines of the fleet of machines 7102; ona selected controller corresponding to one of the machines of the fleetof machines 7102; and on a system controller 6902 communicativelycoupled to the number of the controllers corresponding to machines ofthe fleet of machines 7102 (e.g., consistent with the depiction of FIG.71).

Referencing FIG. 70, an example procedure 7000 includes an operation7002 to interpret a number of attention-related resources available onan attention market, an operation 7006 to determine an attention-relatedresource acquisition value based on a cost parameter of at least one ofthe number of attention-related resources, and an operation 7008 tosolicit an attention-related resource in response to theattention-related resource acquisition value. In certain embodiments,the example procedure 7000 further includes an operation 7004 todetermine a cost parameter and/or an effectiveness parameter for one ormore of the attention-relate resources, and to determine theattention-related resource acquisition value further in response to thecost parameter and/or the effectiveness parameter.

An example procedure 7000 further includes the operation 7008 to performthe soliciting the attention-related resource by performing an operationsuch as: purchasing the attention-related resource from the attentionmarket; selling the attention-related resource to the attention market;making an offer to sell the attention-related resource to a secondintelligent agent; and/or making an offer to purchase theattention-related resource to the second intelligent agent. An exampleprocedure 7000 further includes where the cost parameter of at least oneof the number of attention-related resources includes a future predictedcost of the at least one of the number of attention-related resources,where the procedure 7000 further includes determining theattention-related resource acquisition value in response to a comparisonof a first cost on a spot market with the cost parameter. An exampleprocedure 7000 further includes an operation to interpret a social mediadata source and an operation to determine, in response to the socialmedia data source: a future predicted cost of the at least one of thenumber of attention-related resources, and to utilize the futurepredicted cost as the cost parameter; and/or an effectiveness parameterof the at least one of the number of attention-related resources. Anexample procedure 7000 further includes where the operation 7006 todetermine the attention-related resource acquisition value is furtherbased on the at least one of the future predicted cost or theeffectiveness parameter determined in response to the social media datasource.

Referencing FIG. 72, an example procedure 7200 includes an operation7202 to interpret a number of attention-related resources available onan attention market, an operation 7204 to determine an attention-relatedresource acquisition value for each machine of a fleet of machines basedon a cost parameter of at least one of the number of attention-relatedresources, and further based on a core task for each of a correspondingmachine of the fleet of machines. The example procedure 7200 furtherincludes an operation 7208 to determine an aggregate attention-relatedresource purchase value in response to the number of attention-relatedresource acquisition values corresponding to each machine of the fleetof the machines, and an operation 7210 to solicit an attention-relatedresource in response to the aggregate attention-related resourcepurchase value.

Certain further aspects of an example procedure 7200 are describedfollowing, any one or more of which may be present in certainembodiments. An example procedure 7200 includes where the cost parameterof at least one of the number of attention-related resources includes afuture predicted cost of the at least one of the number ofattention-related resources, and where the procedure further includes anoperation to determine each attention-related resource acquisition valuein response to a comparison of a first cost on a spot market forattention-related resources with the cost parameter. An exampleprocedure 7200 further includes an operation 7206 to interpret a socialmedia data source to determine, in response to the social media datasource, an adjustment for the cost parameter and/or the effectivenessparameter, and to utilize the adjusted cost parameter and/oreffectiveness parameter to determine the aggregate attention-relatedresource purchase value.

In embodiments, provided herein is a transaction-enabling system havingan intelligent agent that is configured to solicit the attentionresources of another external intelligent agent. In embodiments,provided herein is a transaction-enabling system having an intelligentagent that is configured to solicit the attention resources of anotherexternal intelligent agent and having a machine that automaticallypurchases attention resources in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having anintelligent agent that is configured to solicit the attention resourcesof another external intelligent agent and having a fleet of machinesthat automatically aggregate purchasing in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having an intelligent agent that is configured to solicit theattention resources of another external intelligent agent and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having an intelligent agent that isconfigured to solicit the attention resources of another externalintelligent agent and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a facility production outcome. In embodiments, provided hereinis a transaction-enabling system having an intelligent agent that isconfigured to solicit the attention resources of another externalintelligent agent and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having an intelligent agent that isconfigured to solicit the attention resources of another externalintelligent agent and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having an intelligent agent that isconfigured to solicit the attention resources of another externalintelligent agent and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having an intelligent agent that isconfigured to solicit the attention resources of another externalintelligent agent and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having an intelligent agent that isconfigured to solicit the attention resources of another externalintelligent agent and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having anintelligent agent that is configured to solicit the attention resourcesof another external intelligent agent and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to generate an indication that a current or prospectivecustomer should be contacted about an output that can be provided by thefacility. In embodiments, provided herein is a transaction-enablingsystem having an intelligent agent that is configured to solicit theattention resources of another external intelligent agent and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having an intelligent agent that is configured to solicit theattention resources of another external intelligent agent and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of input resources. In embodiments, provided herein isa transaction-enabling system having an intelligent agent that isconfigured to solicit the attention resources of another externalintelligent agent and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having anintelligent agent that is configured to solicit the attention resourcesof another external intelligent agent and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having an intelligent agent that isconfigured to solicit the attention resources of another externalintelligent agent and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having an intelligent agent that isconfigured to solicit the attention resources of another externalintelligent agent and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of parameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases attention resources in a forwardmarket for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases attention resources in a forward market for attention andhaving a fleet of machines that automatically aggregate purchasing in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases attention resources in a forward market for attention andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a likelihoodof a facility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases attention resources in a forward market for attention andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a facilityproduction outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases attention resources in a forward market for attention andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases attention resources in a forwardmarket for attention and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases attention resources in a forward market for attention andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize requisitionand provisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases attention resourcesin a forward market for attention and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases attention resources in a forward market forattention and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizeselection and configuration of an artificial intelligence system toproduce a favorable facility output profile among a set of availableartificial intelligence systems and configurations. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases attention resources in a forward market forattention and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases attention resources in a forward market forattention and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases attention resources in a forward market forattention and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases attention resources in a forwardmarket for attention and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases attention resourcesin a forward market for attention and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to anoutput parameter. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases attention resources in a forward market for attention andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a utilization parameter for the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases attention resourcesin a forward market for attention and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of parameters received from a digital twinfor the facility.

In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing in a forwardmarket for attention. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a likelihoodof a facility production outcome. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a facilityproduction outcome. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing in a forwardmarket for attention and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize requisitionand provisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a forward market for attention and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a forwardmarket for attention and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a forwardmarket for attention and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a forwardmarket for attention and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to at least one of aninput resource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a forward market forattention and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a forwardmarket for attention and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a forward market for attention and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a utilization parameter for the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a forward market for attention and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

Referencing FIG. 73, an example transaction-enabling system 7300includes a production facility 7302 including a core task, where thecore task includes a production task. The system 7300 further includes acontroller 7304 including a facility description circuit 7306 structuredto interpret a plurality of historical facility parameter values 7308and a corresponding plurality of historical facility outcome values7310, and a facility prediction circuit 7312 structured to operate anadaptive learning system 7314, wherein the adaptive learning system 7314is configured to train a facility production predictor 7316 in responseto the plurality of historical facility parameter values 7308 and thecorresponding plurality of historical facility outcome values 7310. Theexample facility description circuit 7306 is further structured tointerpret a plurality of present state facility parameter values 7318;and wherein the facility prediction circuit 7312 is further structuredto operate the adaptive learning system 7314 to predict a present statefacility outcome value 7320 in response to the number of present statefacility parameter values 7318. An example and non-limiting facilityproduction predictor 7316 includes a facility model providing for aprediction of a facility outcome value (e.g., a timing, volume, and/orquality parameter) in response to parameters of the facility. In certainembodiments, the adaptive learning system 7314 utilizes machinelearning, an expert system, and/or AI operations to determine inputvalues that are related to the outcome values, to add or remove inputvalues that are found to be more consistently or less consistentlypredictive of facility outcomes, and/or to add relationships (e.g.,equations, functions, cut-off values, thresholds, etc.) between theinput values and the outcome values. The adaptive learning system 7314allows for improvement of the predicted outcome values over time, andfurther allows for the connection of parameters as predictive of theoutcome values that may not ordinarily be included within a standardmodel that might be built—for example in a particular facility, the dateof delivery of materials may be relevant to predicting outcomes for theproduction facility 7302, where a standard model may focus solely onproduction parameters due to constraints in the thinking and experienceof personnel that build and tune the model. Additionally, standardmodels are expensive and difficult to adapt to changing conditions atthe production facility 7302, such as aging of equipment, changing ofpersonnel, and/or changing of production processes, volumes, andproducts.

In certain embodiments, the present state facility outcome value 7320includes a facility production outcome, such as a volume, time ofcompletion, and/or a quality parameter. Any other aspect of theproduction facility 7302 may additionally or alternatively be a presentstate facility outcome value 7320, including without limitation valuessuch as downtime predictions, equipment or process fault or failurepredictions, overtime predictions, waste material generationpredictions, and the like. In certain embodiments, the present statefacility outcome value 7320 includes a facility production outcomeprobability distribution, such as a confidence interval, high and lowrange targets, and/or a mean and standard deviation (or otherstatistical description, such as a curve, or a non-normal distribution)for an outcome value. In certain embodiments, the present state facilityoutcome value 7320 may further include information such as: whichpresent state facility parameter values 7318 have the greatest effect onthe predicted outcome values 7320, which present state facilityparameter values 7318 have driven recent change in the predicted presentstate facility outcome values 7320, and/or which input sources drive thegreatest uncertainty within a range of present state facility outcomevalues 7320. An example present state facility outcome value 7320includes at least one value such as: a production volume description ofthe production task; a production quality description of the productiontask; a facility resource utilization description; an input resourceutilization description; and a production timing description of theproduction task. Example and non-limiting production volume descriptionsinclude a total production volume, a production volume relative to atarget value, and/or a specific production volume such as the volumeproduced per unit of resource input, personnel time utilized, productiontooling life utilization, or the like. Example and non-limiting qualitydescriptions include: a description of products having acceptablequality (e.g., fit for purpose); a description of a scrap or waste ratein products; a distribution of a product parameter such as a test value,tolerance, and/or measurement; and/or a description of a qualitative orcategorical distribution of the products. Example and non-limitingfacility resource utilization descriptions include: a description ofenergy consumption for the production facility; a description ofpersonnel utilization of the facility; a description of consumption of asecondary resource for the facility (e.g., recycling, waste production,parking utilization, and/or consumption or production of energycredits); a description of production tooling or other facility assetconsumption; trends in any of the foregoing; and/or changes in any ofthe foregoing. Example and non-limiting input resource utilizationdescriptions include: a description of a raw resource or input productutilization; a description of capital investment for the productionfacility; and/or a description of operating costs for the productionfacility. The examples herein are not limiting to any other aspect ofthe present disclosure, and are provided only for illustration.

An example facility description circuit 7306 further interpretshistorical external data from at least one external data source 7322(e.g., a prior existing production facility and/or an offset productionfacility), where the adaptive learning system 7314 is further configuredto train the facility production predictor 7316 in response to thehistorical external data. Example and non-limiting external data sourcesinclude: a social media data source; a behavioral data source; a spotmarket price for an energy source; and/or a forward market price for anenergy source. An example facility description circuit 7306 furtherinterprets present external data from at least one external data source,where the adaptive learning system 7314 is further configured to predictthe present state facility outcome value 7320 further in response to thepresent external data.

Referencing FIG. 74, an example procedure 7400 includes an operation7402 to interpret a plurality of historical facility parameter valuesand a corresponding plurality of historical facility outcome values; anoperation 7408 to operate an adaptive learning system, thereby traininga facility production predictor in response to the plurality ofhistorical facility parameter values and the corresponding plurality ofhistorical facility outcome values; an operation 7410 to interpret anumber of present state facility parameter values; and an operation 7412to operate the adaptive learning system to predict a present statefacility outcome value in response to the plurality of present statefacility parameter values.

An example procedure 7400 further includes an operation 7406 tointerpret historical external data from at least one external datasource, and to operate the adaptive learning system to further train thefacility production predictor in response to the historical externaldata. An example procedure 7400 includes the operation 7406 to interpretpresent external data from the at least one data source, and operatingthe adaptive learning system to predict the present state facilityoutcome value further in response to the present external data.

Referencing FIG. 75, an example transaction-enabling system 7500includes a production facility 7302 including a core task, and acontroller 7304, including: a facility description circuit 7306structured to interpret a plurality of historical facility parametervalues 7308 and a corresponding plurality of historical facility outcomevalues 7310; a facility prediction circuit 7312 structured to operate anadaptive learning system 7314, wherein the adaptive learning system isconfigured to train a facility resource allocation circuit 7502 inresponse to the plurality of historical facility parameter values 7308and the corresponding plurality of historical facility outcome values7310; wherein the facility description circuit 7306 is furtherstructured to interpret a plurality of present state facility parametervalues 7318; and where the trained facility resource allocation circuit7502 is further structured to adjust, in response to the plurality ofpresent state facility parameter values 7318, a plurality of facilityresource values (e.g., by providing adjustments to the facility resourcevalues 7504).

Example and non-limiting facility resource values include: aprovisioning and an allocation of facility energy resources; and/or aprovisioning and an allocation of facility compute resources. An exampletrained facility resource allocation circuit 7502 further adjusts theplurality of facility resource values by producing and/or selecting afavorable facility resource utilization profile from among a set ofavailable facility resource configuration profiles 7506. An exampletrained facility resource allocation circuit 7502 is further structuredto adjust the plurality of facility resource values 7504 by one ofproducing or selecting a favorable facility resource output selectionfrom among a set of available facility resource output values. Anexample trained facility resource allocation circuit 7502 is furtherstructured to adjust the plurality of facility resource values 7504 byproducing and/or selecting a favorable facility resource input profilefrom among a set of available facility resource input profiles. Anexample trained facility resource allocation circuit 7502 is furtherstructured to adjust the plurality of facility resource values 7504 byproducing or selecting a favorable facility resource configurationprofile 7506 from among a set of available facility resourceconfiguration profiles.

An example facility description circuit 7306 is further structured tointerpret historical external data from at least one external datasource 7322, and wherein the adaptive learning system 7314 is furtherconfigured to train the facility resource allocation circuit 7502 inresponse to the historical external data. Example and non-limitingexternal data source(s) include at least one data source such as: asocial media data source; a behavioral data source; a spot market pricefor an energy source; and/or a forward market price for an energysource. An example facility description circuit 7306 further interpretspresent external data from the at least one external data source 7322,and wherein the trained facility resource allocation circuit 7502 isfurther structured to adjust the plurality of facility resource values7504 in response to the present external data.

Referencing FIG. 76, an example procedure 7600 includes an operation7602 to interpret a plurality of historical facility parameter valuesand a corresponding plurality of historical facility outcome values; anoperation 7606 to operate an adaptive learning system, thereby traininga facility resource allocation circuit in response to the plurality ofhistorical facility parameter values and the corresponding plurality ofhistorical facility outcome values; and an operation 7608 to interpret aplurality of present state facility parameter values. The exampleprocedure 7600 further includes an operation 7610 to adjust, in responseto the plurality of present state facility parameter values, a pluralityof facility resource values. In certain embodiments, for example wherethe operations 7610 to adjust the facility resource values by selectingamong available profiles for the facility, an example procedure 7600further includes operations to update the set of available facilityresource profiles, for example by removing an ineffective, unused, orlightly used facility resource profile, and/or by adding a new facilityresource profile to the set of available facility resource profiles. Anexample procedure 7600 further includes an operation 7604 to interprethistorical external data and/or present external data, where theoperation 7606 further includes training the facility resourceallocation circuit in response to the historical external data and/orpresent external data.

Referencing FIG. 77, an example transaction-enabling system 7700includes a production facility 7302 including a core task; a controller7304, including: a facility description circuit 7306 structured tointerpret a plurality of historical facility parameter values 7308 and acorresponding plurality of historical facility outcome values 7310; afacility prediction circuit 7312 structured to operate an adaptivelearning system 7314, wherein the adaptive learning system 7314 isconfigured to train a facility artificial intelligence (AI)configuration circuit 7702 in response to the plurality of historicalfacility parameter values 7308 and the corresponding plurality ofhistorical facility outcome values 7310; where the facility descriptioncircuit 7306 is further structured to interpret a plurality of presentstate facility parameter values 7318. The example system 7700 includeswhere the trained facility AI configuration circuit 7702 is furtherstructured to adjust, in response to the plurality of present statefacility parameter values, a configuration of a facility AI component7704 to produce a favorable facility output value. A favorable facilityoutput value includes, without limitation, an improvement and/oroptimization of any facility output described in the present disclosure,including at least a production related output and/or a resourceutilization output.

An example trained facility AI configuration circuit 7702 furtheradjusts the configuration of the facility AI component by one ofproducing or selecting a favorable facility AI component configurationprofile 7706 from among a set of available facility AI componentconfiguration profiles. An example favorable facility output valueincludes, without limitation, an output value such as: a productionvolume description of the core task; a production quality description ofthe core task; a facility resource utilization description; an inputresource utilization description; and/or a production timing descriptionof the core task. An example facility description circuit 7306 isfurther structured to interpret historical external data from at leastone external data source 7322, and wherein the adaptive learning system7314 is further configured to train the facility AI configurationcircuit 7702 in response to the historical external data. An examplesystem 7700 includes where the external data source includes at leastone data source such as: a social media data source; a behavioral datasource; a spot market price for an energy source; and/or a forwardmarket price for an energy source. An example facility descriptioncircuit 7306 further interprets present external data from the at leastone external data source 7322, and wherein the trained facility AIconfiguration circuit 7702 is further structured to adjust theconfiguration of the facility AI component 7704 in response to thepresent external data.

Referencing FIG. 78, an example procedure 7800 includes an operation7802 to interpret a plurality of historical facility parameter valuesand a corresponding plurality of historical facility outcome values; anoperation 7806 to operate an adaptive learning system, thereby traininga facility artificial intelligence (AI) configuration circuit inresponse to the plurality of historical facility parameter values andthe corresponding plurality of historical facility outcome values; anoperation 7808 to interpret a plurality of present state facilityparameter values; and an operation 7810 to operate the trained facilityAI configuration circuit to adjust, in response to the plurality ofpresent state facility parameter values, a configuration of a facilityAI component to produce a favorable facility output value. An exampleprocedure 7800 further includes an operation 7804 to interprethistorical external data and/or present external data from at least onedata source, wherein the operation 7806 to operate the trained facilityAI configuration circuit is configure the AI facility component isfurther in response to the historical external data and/or the presentexternal data.

Further referencing FIG. 73, an example system includes wherein the coretask includes a customer relevant output; and the trained facilityproduction predictor 7316 is configured to determine a customer contactindicator 7324 in response to the plurality of present state facilityparameter values 7318; and wherein the controller 7304 further includesa customer notification circuit 7326 structured to provide anotification 7328 to a customer in response to the customer contactindicator 7324. In certain embodiments, the customer includes one of acurrent customer and/or a prospective customer. In certain embodiments,determining the customer contact indicator includes performing at leastone operation such as: determining whether the customer relevant outputwill meet a volume request from the customer; determining whether thecustomer relevant output will meet a quality request from the customer;determining whether the customer relevant output will meet a timingrequest from the customer; and/or determining whether the customerrelevant output will meet an optional request from the customer.

Referencing FIG. 79, an example procedure 7900 an operation 7902 tointerpret a number of historical facility parameter values and acorresponding plurality of historical facility outcome values; anoperations 7906 to operate an adaptive learning system, thereby traininga facility production predictor in response to the plurality ofhistorical facility parameter values and the corresponding plurality ofhistorical facility outcome values; an operation 7908 to interpret anumber of present state facility parameter values; an operation 7910 tooperate the trained facility production predictor to determine acustomer contact indicator in response to the plurality of present statefacility parameter values; and an operation 7912 to provide anotification to a customer in response to the customer contactindicator. An example procedure 7900 further includes an operation 7904to interpret historical external data and/or present external data fromat least one external data source, wherein the operation 7910 to operatethe trained facility AI configuration circuit includes further configurethe AI facility component in response to the historical external dataand/or the present external data.

Referencing FIG. 80, an example transaction-enabling system 8000includes a facility 8002 having an energy source and/or an energyutilization requirement, and a compute resource and/or compute task. Anexample facility 8002 is an energy and compute facility—for example aserver, computing cluster, or the like, that may be paired with anenergy source, and/or have an energy utilization requirement foroperations of the facility 8002. The example system 8000 includes acontroller 7304 having a facility description circuit 7306 structured tointerpret detected conditions 8004, wherein the detected conditions 8004include at least one condition such as: an input resource for thefacility; a facility resource; an output parameter for the facility;and/or an external condition related to an output of the facility 8002.The example controller 7304 further includes a facility configurationcircuit 8010 structured to operate an adaptive learning system 7314,wherein the adaptive learning system 7314 is configured to adjust afacility configuration 8006 based on the detected conditions 8004. Anexample adaptive learning system 7314 includes at least one of a machinelearning system and/or an artificial intelligence (AI) system. Anexample adaptive learning system 7314 adjusts the facility configuration8006 by performing a purchase and/or sale transaction on a market 8008,such as: performing a purchase or sale transaction on one of an energyspot market or an energy forward market; performing a purchase or saletransaction on one of a compute resource spot market or a computeresource forward market; and performing a purchase or sale transactionon one of an energy credit spot market or an energy credit forwardmarket. An example facility 8002 further includes a networking task, andwherein the adaptive learning system 7314 adjusts the facilityconfiguration 8006 by performing a purchase and/or sale transaction on amarket 8008, such as: performing a purchase or sale transaction on oneof a network bandwidth spot market or a network bandwidth forwardmarket; and/or performing a purchase or sale transaction on one of aspectrum spot market or a spectrum forward market. An example adaptivelearning system 7314 adjusts the facility configuration 8006 byadjusting at least one task of the facility 8002 to reduce the energyutilization requirement of the facility.

Referencing FIG. 81, an example procedure 8100 includes an operation8102 to interpret detected conditions relative to a facility, where thedetected conditions include at least one condition such as: an inputresource for the facility; a facility resource; an output parameter forthe facility; and/or an external condition related to an output of thefacility. In certain embodiments, operation 8102 includes interpretingdetected conditions relating to a set of input resources for thefacility. In certain embodiments, the detected conditions relate to atleast one resource of the facility. In certain embodiments, the detectedconditions relate to an output parameter for the facility. In certainembodiments, the detected conditions relate to a utilization parameterfor an output of the facility. The example procedure 8100 furtherincludes an operation 8104 to determine one of a spot market or forwardmarket price for a facility resource. The example procedure 8100 furtherincludes an operation 8106 to operate an adaptive learning system toadjust a facility configuration based on the detected conditions. Anexample procedure 8100 further includes an operation 8108 to adjust thefacility configuration by executing a transaction on a spot marketand/or a forward market for a facility resource. In certain embodiments,the facility resource includes one or more of: an energy resource, acompute resource, an energy credit resource, and/or a network bandwidthresource. In certain embodiments, the facility resource includes aspectrum resource.

Referencing FIG. 82, an example transaction-enabling system 8200includes a facility 8002 having at least one of an energy source or anenergy utilization requirement. An example facility 8002 is an energyand compute facility. The example transaction-enabling system 8200includes a controller 7304 having a facility description circuit 7306structured to interpret detected conditions 8004, wherein the detectedconditions relate to a set of input resources for the facility 8002. Theexample controller 7304 further includes a facility configurationcircuit 8010 structured to operate an adaptive learning system 7314,wherein the adaptive learning system is configured to adjust a facilityconfiguration 8006 based on the detected conditions. An exampletransaction-enabling system 8200 further includes the adaptive learningsystem 7314 having at least one of a machine learning system and/or anAI system. An example adaptive learning system 7314 adjusts the facilityconfiguration 8006 by performing a purchase and/or a sale transaction ona market 8008, such as a spot market and/or a forward market for afacility resource. Example and non-limiting facility resources include:an energy resource; a compute resource; an energy credit resource; anetwork bandwidth resource; and/or a spectrum resource. In certainembodiments, the facility 8002 further includes a networking task. Incertain embodiments, the adaptive learning system 7314 further adjustsat least one facility task 8202 to change an input resource requirementfor the facility 8002, and/or adjusts at least one facilityconfiguration 8006 to change the input resource requirement for thefacility 8002. In certain embodiments, the facility 8002 furtherincludes at least one additional facility resource; where the adaptivelearning system 7314 adjusts the facility configuration 8006 byadjusting the compute resource, and further adjusting at the at leastone additional facility resource. Example and non-limiting additionalfacility resources include a network resource, a data storage resource,and/or a spectrum resource.

In certain embodiments, the detected conditions 8004 include an outputparameter for the facility 8002. In certain further embodiments, theadaptive learning system 7314 adjusts the facility configuration 8006and/or the facility tasks 8202 to provide at least one of: an increasedfacility output volume, an increased facility quality value, and/or anadjusted facility output time value.

In certain embodiments, the detected conditions 8004 include autilization parameter for an output of the facility 8002. In certainfurther embodiments, the adaptive learning system 7314 adjusts thefacility configuration 8006 and/or the facility tasks 8202 to adjust atleast one task of the facility 8002 to reduce the utilization parameterfor output of the facility 8002.

Referencing FIG. 83, an example transaction-enabling system 8300includes an energy and compute facility 8002 including: at least one ofa compute task or a compute resource; and at least one of an energysource or an energy utilization requirement. The exampletransaction-enabling system 8300 further includes a controller 7304having a facility model circuit 8302 that operates a digital twin 8304for the facility 8002, a facility description circuit 7306 thatinterprets a set of parameters 8306 from the digital twin for thefacility; and a facility configuration circuit 8010 structured tooperate an adaptive learning system 7314, where the adaptive learningsystem 7314 is configured to adjust a facility configuration 8006 basedon the set of parameters 8306 from the digital twin for the facility8002. An example adaptive learning system 7314 includes a machinelearning system and/or an AI system. An example adaptive learning system7314 adjusts the facility configuration 8006 by performing a purchasetransaction and/or a sale transaction of a resource on a market 8008.Example and non-limiting markets 8008 include a spot market and/or aforward market for a resource. Example and non-limiting resourcesinclude an energy resource, a networking resource, a network bandwidthresource, an energy credit resource, and/or a spectrum resource. Anexample facility 8002 further includes a networking task.

An example system further includes the facility description circuit 7306interpreting the detected conditions 8004, where the detected conditions8004 include one or more of: an input resource for the facility 8002, afacility resource, an output parameter for the facility 8002, and/or anexternal condition related to an output of the facility. An examplefacility model circuit 8302 is further structured to update the digitaltwin 8304 in response to the detected conditions 8004.

Referencing FIG. 84, an example procedure 8400 includes an operation8402 to operate a model that is, or that acts as, a digital twin of afacility. An example facility includes an energy resource and/or anenergy utilization requirement, a compute resource and/or a computetask, and/or a network resource and/or a network task. The exampleprocedure 8400 further includes an operation 8404 to interpret a set ofparameters from the digital twin for the facility, an operation tointerpret present state facility parameters/values 8406 and an operation8408 to operate an adaptive learning system to adjust a facilityconfiguration and/or a facility task based on the set of parameters forthe digital twin, and/or based on facility parameter values as detectedconditions for the facility. An example procedure 8400 includes anoperation 8410 to update the digital twin based on the facilityparameter values. Example and non-limiting facility parameter valuesinclude values such as: an input resource for the facility; a facilityresource; an output parameter for the facility; and/or an externalcondition related to an output of the facility.

In embodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a likelihood of a facility production outcome. In embodiments,provided herein is an information technology system for providing datato an intelligent energy and compute facility resource management systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a likelihoodof a facility production outcome and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a facility production outcome. Inembodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a likelihood of a facility production outcome and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a likelihood of a facility production outcome and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a likelihood of a facility production outcome and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize requisition andprovisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is an information technologysystem for providing data to an intelligent energy and compute facilityresource management system having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto predict a likelihood of a facility production outcome and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a likelihood of a facility production outcome and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis an information technology system for providing data to an intelligentenergy and compute facility resource management system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to generate an indication that a current or prospectivecustomer should be contacted about an output that can be provided by thefacility. In embodiments, provided herein is an information technologysystem for providing data to an intelligent energy and compute facilityresource management system having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto predict a likelihood of a facility production outcome and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is an information technologysystem for providing data to an intelligent energy and compute facilityresource management system having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto predict a likelihood of a facility production outcome and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of input resources. In embodiments, provided herein isan information technology system for providing data to an intelligentenergy and compute facility resource management system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is an information technologysystem for providing data to an intelligent energy and compute facilityresource management system having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto predict a likelihood of a facility production outcome and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is aninformation technology system for providing data to an intelligentenergy and compute facility resource management system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a utilizationparameter for the output of the facility. In embodiments, providedherein is an information technology system for providing data to anintelligent energy and compute facility resource management systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a likelihoodof a facility production outcome and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of parameters received from a digital twinfor the facility.

In embodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a facility production outcome. In embodiments, provided hereinis an information technology system for providing data to an intelligentenergy and compute facility resource management system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizeprovisioning and allocation of energy and compute resources to produce afavorable facility resource utilization profile among a set of availableprofiles. In embodiments, provided herein is an information technologysystem for providing data to an intelligent energy and compute facilityresource management system having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto predict a facility production outcome and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a facility production outcome and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize requisition and provisioning of availableenergy and compute resources to produce a favorable facility inputresource profile among a set of available profiles. In embodiments,provided herein is an information technology system for providing datato an intelligent energy and compute facility resource management systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a facilityproduction outcome and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is an informationtechnology system for providing data to an intelligent energy andcompute facility resource management system having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a facility production outcome and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis an information technology system for providing data to an intelligentenergy and compute facility resource management system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility. In embodiments,provided herein is an information technology system for providing datato an intelligent energy and compute facility resource management systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a facilityproduction outcome and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to at least one of aninput resource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is an information technology system for providing datato an intelligent energy and compute facility resource management systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a facilityproduction outcome and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of inputresources. In embodiments, provided herein is an information technologysystem for providing data to an intelligent energy and compute facilityresource management system having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto predict a facility production outcome and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of facility resources. In embodiments, provided hereinis an information technology system for providing data to an intelligentenergy and compute facility resource management system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to an output parameter. In embodiments,provided herein is an information technology system for providing datato an intelligent energy and compute facility resource management systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a facilityproduction outcome and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a utilizationparameter for the output of the facility. In embodiments, providedherein is an information technology system for providing data to anintelligent energy and compute facility resource management systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a facilityproduction outcome and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of parameters received from a digital twin for thefacility.

In embodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles. In embodiments, provided herein is an informationtechnology system for providing data to an intelligent energy andcompute facility resource management system having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource utilizationprofile among a set of available profiles and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is aninformation technology system for providing data to an intelligentenergy and compute facility resource management system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profilesand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimizeconfiguration of available energy and compute resources to produce afavorable facility resource configuration profile among a set ofavailable profiles. In embodiments, provided herein is an informationtechnology system for providing data to an intelligent energy andcompute facility resource management system having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource utilizationprofile among a set of available profiles and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis an information technology system for providing data to an intelligentenergy and compute facility resource management system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profilesand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility. In embodiments,provided herein is an information technology system for providing datato an intelligent energy and compute facility resource management systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profilesand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofdetected conditions relating to at least one of an input resource, afacility resource, an output parameter and an external condition relatedto the output of the facility. In embodiments, provided herein is aninformation technology system for providing data to an intelligentenergy and compute facility resource management system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profilesand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofdetected conditions relating to a set of input resources. Inembodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is an information technologysystem for providing data to an intelligent energy and compute facilityresource management system having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize provisioning and allocation of energy and compute resourcesto produce a favorable facility resource utilization profile among a setof available profiles and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to an output parameter.In embodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a utilizationparameter for the output of the facility. In embodiments, providedherein is an information technology system for providing data to anintelligent energy and compute facility resource management systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profilesand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofparameters received from a digital twin for the facility.

In embodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is an informationtechnology system for providing data to an intelligent energy andcompute facility resource management system having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource outputselection among a set of available outputs and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize requisition andprovisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is an information technologysystem for providing data to an intelligent energy and compute facilityresource management system having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize provisioning and allocation of energy and compute resourcesto produce a favorable facility resource output selection among a set ofavailable outputs and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is an informationtechnology system for providing data to an intelligent energy andcompute facility resource management system having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource outputselection among a set of available outputs and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis an information technology system for providing data to an intelligentenergy and compute facility resource management system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility. In embodiments,provided herein is an information technology system for providing datato an intelligent energy and compute facility resource management systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to at least one of an input resource, a facilityresource, an output parameter and an external condition related to theoutput of the facility. In embodiments, provided herein is aninformation technology system for providing data to an intelligentenergy and compute facility resource management system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of input resources. In embodiments,provided herein is an information technology system for providing datato an intelligent energy and compute facility resource management systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of facility resources. In embodiments,provided herein is an information technology system for providing datato an intelligent energy and compute facility resource management systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter. In embodiments, providedherein is an information technology system for providing data to anintelligent energy and compute facility resource management systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a utilization parameter for the output of thefacility. In embodiments, provided herein is an information technologysystem for providing data to an intelligent energy and compute facilityresource management system having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize provisioning and allocation of energy and compute resourcesto produce a favorable facility resource output selection among a set ofavailable outputs and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of parameters received from a digital twin for the facility.

In embodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is aninformation technology system for providing data to an intelligentenergy and compute facility resource management system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize requisition andprovisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizeconfiguration of available energy and compute resources to produce afavorable facility resource configuration profile among a set ofavailable profiles. In embodiments, provided herein is an informationtechnology system for providing data to an intelligent energy andcompute facility resource management system having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize requisition and provisioning of availableenergy and compute resources to produce a favorable facility inputresource profile among a set of available profiles and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis an information technology system for providing data to an intelligentenergy and compute facility resource management system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize requisition andprovisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility. In embodiments,provided herein is an information technology system for providing datato an intelligent energy and compute facility resource management systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize requisitionand provisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to at least one of an input resource, afacility resource, an output parameter and an external condition relatedto the output of the facility. In embodiments, provided herein is aninformation technology system for providing data to an intelligentenergy and compute facility resource management system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize requisition andprovisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of input resources. Inembodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is an information technologysystem for providing data to an intelligent energy and compute facilityresource management system having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to an output parameter.In embodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a utilizationparameter for the output of the facility. In embodiments, providedherein is an information technology system for providing data to anintelligent energy and compute facility resource management systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize requisitionand provisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofparameters received from a digital twin for the facility.

In embodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is an informationtechnology system for providing data to an intelligent energy andcompute facility resource management system having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis an information technology system for providing data to an intelligentenergy and compute facility resource management system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility. In embodiments,provided herein is an information technology system for providing datato an intelligent energy and compute facility resource management systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimizeconfiguration of available energy and compute resources to produce afavorable facility resource configuration profile among a set ofavailable profiles and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to at least one of aninput resource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is an information technology system for providing datato an intelligent energy and compute facility resource management systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimizeconfiguration of available energy and compute resources to produce afavorable facility resource configuration profile among a set ofavailable profiles and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of inputresources. In embodiments, provided herein is an information technologysystem for providing data to an intelligent energy and compute facilityresource management system having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is an information technologysystem for providing data to an intelligent energy and compute facilityresource management system having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to an output parameter.In embodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a utilizationparameter for the output of the facility. In embodiments, providedherein is an information technology system for providing data to anintelligent energy and compute facility resource management systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimizeconfiguration of available energy and compute resources to produce afavorable facility resource configuration profile among a set ofavailable profiles and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of parameters received from a digital twin for thefacility.

In embodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is aninformation technology system for providing data to an intelligentenergy and compute facility resource management system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is an information technologysystem for providing data to an intelligent energy and compute facilityresource management system having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of input resources. In embodiments,provided herein is an information technology system for providing datato an intelligent energy and compute facility resource management systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize selectionand configuration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of facility resources. In embodiments, provided hereinis an information technology system for providing data to an intelligentenergy and compute facility resource management system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is aninformation technology system for providing data to an intelligentenergy and compute facility resource management system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility. Inembodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of parametersreceived from a digital twin for the facility.

In embodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility. Inembodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to at least one of an input resource, a facilityresource, an output parameter and an external condition related to theoutput of the facility. In embodiments, provided herein is aninformation technology system for providing data to an intelligentenergy and compute facility resource management system having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to a setof input resources. In embodiments, provided herein is an informationtechnology system for providing data to an intelligent energy andcompute facility resource management system having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to generate an indication that a current or prospectivecustomer should be contacted about an output that can be provided by thefacility and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources. Inembodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter. In embodiments, providedherein is an information technology system for providing data to anintelligent energy and compute facility resource management systemhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility. Inembodiments, provided herein is an information technology system forproviding data to an intelligent energy and compute facility resourcemanagement system having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of parametersreceived from a digital twin for the facility.

In embodiments, provided herein is a system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is a system having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of input resources. Inembodiments, provided herein is a system having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to atleast one of an input resource, a facility resource, an output parameterand an external condition related to the output of the facility andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of facility resources. In embodiments,provided herein is a system having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to at least one of aninput resource, a facility resource, an output parameter and an externalcondition related to the output of the facility and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is asystem having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to at least one of an input resource, afacility resource, an output parameter and an external condition relatedto the output of the facility and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a utilizationparameter for the output of the facility. In embodiments, providedherein is a system having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of input resources. In embodiments, provided herein isa system having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of input resources and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of facility resources. In embodiments, provided hereinis a system having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of input resources and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is asystem having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of input resources and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility. Inembodiments, provided herein is a system having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to a setof input resources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of parameters received from a digital twin for thefacility.

In embodiments, provided herein is a system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of facility resources. In embodiments, provided hereinis a system having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter. In embodiments, providedherein is a system having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of facility resources andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a utilization parameter for the output of thefacility. In embodiments, provided herein is a system having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of facility resources and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is asystem having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to an output parameter and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility. Inembodiments, provided herein is a system having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to anoutput parameter and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of parameters received from a digital twin for the facility.

In embodiments, provided herein is a system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility. Inembodiments, provided herein is a system having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to autilization parameter for the output of the facility and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a system having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

Referencing FIG. 85, an example transaction-enabling system 8500includes a machine 8502 having an associated regenerative energyfacility 8504, the machine 8502 having a requirement for at least one ofa compute task, a networking task, and/or an energy consumption task.The example system 8500 further includes a controller 8506 including: anenergy requirement circuit 8508 structured to determine an amount ofenergy 8510 for the machine to service the at least one of the computetask, the networking task, and/or the energy consumption task inresponse to the requirement for the at least one of the compute task,the networking task, and/or the energy consumption task. The examplecontroller 8506 further includes an energy distribution circuit 8512structured to adaptively improve an energy delivery of energy 8514produced by the associated regenerative energy facility 8504 between theat least one of the compute task, the networking task, and/or the energyconsumption task.

An example system 8500 includes the energy consumption task comprises asa core task, e.g. of the machine 8502. An example controller 8506further includes an energy market circuit 8516 structured to access anenergy market 8518, and wherein the energy distribution circuit 8516 isfurther structured to adaptively improve the energy delivery of theenergy 8514 produced by the associated regenerative energy facility 8504between the compute task, the networking task, the energy consumptiontask, and/or a sale (e.g., via a transaction 8520) of the energyproduced by the associated regenerative energy facility 8504 on theenergy market 8518. An example energy market 8518 comprises at least oneof a spot market or a forward market. An example controller 8506 furtherincludes where the energy distribution circuit further 8516 comprises atleast one of a machine learning component, an artificial intelligencecomponent, or a neural network component (e.g., as a continuousimprovement component 8522).

Referencing FIG. 86, an example procedure 8600 includes an operation8602 to determine an amount of energy for a machine to service at leastone of a compute task, a networking task, and/or an energy consumptiontask in response to a compute task requirement, a networking taskrequirement, and/or an energy consumption task requirement. The exampleprocedure 8600 further includes an operation 8604 to adaptively improvean energy delivery between the compute task, the networking task, and/orthe energy consumption task, where the improved energy deliver is ofenergy produced by a regenerative energy facility of the machine. Incertain embodiments, the procedure 8600 further includes an operation8606 to access an energy market, and adaptively improving the energydelivery of the energy produced by the regenerative energy facilitybetween: the compute task, the networking task, the energy consumptiontask, and a sale of the energy produced by the regenerative energyfacility on the energy market.

Referencing FIG. 87, an example transaction-enabling system 8700includes a machine 8502 having at least one of a compute taskrequirement, a networking task requirement, and/or an energy consumptiontask requirement; and a controller 8506 including a resource requirementcircuit 8708 structured to determine an amount of a resource 8710 forthe machine to service at least one of the compute task requirement, thenetworking task requirement, and/or the energy consumption taskrequirement, and a forward resource market circuit 8716 structured toaccess a forward resource market 8718. The example controller 8506further includes a resource distribution circuit 8712 structured toexecute a transaction 8720 of the resource on the forward resourcemarket 8718 in response to the determined amount of the resource 8710.An example system 8700 includes the resource being any one or more of acompute resource, a spectrum allocation resource, an energy creditresource, an energy resource, a data storage resource, an energy storageresource, and/or a network bandwidth resource. An example system 8700includes the forward resource market 8718 being a forward market for anyone or more of the example resources. An example embodiment includeswhere the transaction(s) 8720 include buying and/or selling theresource. An example resource distribution circuit 8712 is furtherstructured to adaptively improve one of an output value of the machine8502 or a cost of operation of the machine 8502 using executedtransactions 8720 on the forward resource market 8718. An exampleresource distribution circuit 8712 further includes at least one of amachine learning component, an artificial intelligence component, and/ora neural network component (e.g., as a continuous improvement component8722).

Referencing FIG. 88, an example procedure 8800 includes an operation8802 to determine an amount of a resource for a machine to service atleast one of a compute task requirement, a networking task requirement,and/or an energy consumption task requirement of the machine, and anoperation 8804 to access a forward resource market. The exampleprocedure 8800 further includes an operation 8806 to execute atransaction of the resource on the forward resource market in responseto the determined amount of the resource. In certain embodiments, theprocedure 8800 further includes an operation 8808 to adaptively improvean output value of the machine and/or a cost of operation of the machineusing executed transactions on the forward resource market.

Referencing FIG. 89, an example transaction-enabling system 8900includes a fleet of machines 8902 each having at least one of a computetask requirement, a networking task requirement, and/or an energyconsumption task requirement. The example system 8900 includes acontroller 8506 including a resource requirement circuit 8708 structuredto determine an amount of a resource 8710 for each of the machines toservice the compute task requirements, the networking task requirements,and/or the energy consumption task requirements for each correspondingmachine of the fleet of machines 8902. The example controller 8506further includes a forward resource market circuit 8716 structured toaccess a forward resource market 8718; and a resource distributioncircuit 8712 structured to execute an aggregated transaction 8920 of theresource on the forward resource market 8718 in response to thedetermined amount of the resource 8710 for each of the machines. Anexample system 8900 includes the resource being any one or more of acompute resource, a spectrum allocation resource, an energy creditresource, an energy resource, a data storage resource, an energy storageresource, and/or a network bandwidth resource. An example system 8900includes the forward resource market 8718 being a forward market for anyone or more of the example resources. An example embodiment includeswhere the transaction(s) 8920 include buying and/or selling theresource. An example resource distribution circuit 8712 is furtherstructured to adaptively improve one of an aggregate output value of thefleet of machines or a cost of operation of the fleet of machines usingexecuted aggregated transactions 8920 on the forward resource market8718. In certain embodiments, a resource distribution circuit 8712includes a machine learning component, an artificial intelligencecomponent, and/or a neural network component (e.g., as a continuousimprovement circuit 8722).

Referencing FIG. 90, an example procedure 9000 includes an operation9002 to determine an amount of a resource, for each of machine of afleet of machines, to service at least one of a compute taskrequirement, a networking task requirement, and/or an energy consumptiontask requirement for each corresponding machine of the fleet ofmachines. The example procedure 9000 further includes an operation 9004to access a forward resource market and an operation 9006 to execute anaggregated transaction of the resource on the forward resource market inresponse to the determined amount of the resource for each of themachines. In certain embodiments, an example procedure 9000 includes anoperation 9008 to adaptively improve an aggregate output value of thefleet of machines and/or a cost of operation of the fleet of machinesusing executed aggregated transactions on the forward resource market.

Referencing FIG. 91, an example transaction-enabling system 9100, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a fleet of machines 8902 each having arequirement for at least one of a compute task, a networking task,and/or an energy consumption task. The example system 9100 furtherincludes a controller 8506 having a resource requirement circuit 8708structured to determine an amount of a resource 8710 for each of themachines to service the requirement for the compute task, the networkingtask, and/or the energy consumption task for each corresponding machineof the fleet of machines 8902. The example controller 8506 furtherincludes a resource distribution circuit 9112 structured to adaptivelyimprove a resource utilization 9102 of the resource for each of themachines between the compute task, the networking task, and/or theenergy consumption task for each corresponding machine. Example andnon-limiting resource(s) 8710 include a compute resource, a spectrumallocation resource, an energy credit resource, an energy resource, adata storage resource, an energy storage resource, and/or a networkbandwidth resource. An example resource distribution circuit 9112includes a machine learning component, an artificial intelligencecomponent, and/or a neural network component (e.g., continuousimprovement component 8722). In certain embodiments, the controller 8506includes a resource market circuit 9116 structured to access a market9118 for one or more resources, and where the resource distributioncircuit 9112 improves the resource utilization 9102 by executing one ormore transactions 8920 on a market 9118 for the one or more resources toimprove the resource utilization 9102. In certain embodiments, themarket 9118 may be spot market and/or a forward market for the one ormore resources. In certain embodiments, a transaction 8920 includes apurchase and/or a sale of a resource.

Referencing FIG. 92, an example procedure 9200 includes an operation9202 to determine an amount of a resource, for each of machine of afleet of machines, to service a requirement of at least one of a computetask, a networking task, and/or an energy consumption task for eachcorresponding machine. The procedure 9200 further includes an operation9204 to adaptively improve a resource utilization of the resource foreach of the machines between the compute task, the networking task,and/or the energy consumption task for each corresponding machine.Example and non-limiting operations 9204 include adaptively improving anoutput value of the fleet of machines, and/or a cost of operation of thefleet of machines. Example and non-limiting operations 9204 toadaptively improve the resource utilization include adjusting workloadsfor tasks between machines of the fleet of machines, executing one ormore transactions for a resource on a forward market and/or a spotmarket for a resource, and/or adjusting one or more tasks of a machineof the fleet of machines to adjust a total (or aggregate) resourceutilization of the fleet of machines. Example and non-limitingoperations 9204 to adaptively improve the resource utilization includereducing a total resource utilization, reducing a resource utilizationfor a particular type of resource, and/or time-shifting resourceutilization from a less efficient (e.g., based on cost, capacity,availability, and/or impact of the resource utilization) time frame to amore efficient time frame for the resource utilization. Example andnon-limiting operations 9204 include improving the resource utilizationby adjusting a consumed resource of one type to a consumed resource ofanother type by the machine(s), for example by substituting a secondresource for a first resource, and/or by substituting a second task fora first task, thereby substituting a second resource for a firstresource. Example and non-limiting operations 9204 include executing atransaction related to a second resource in response to a utilization ofa first resource, for example where the second resource can besubstituted for the first resource, and/or where the second resourceprovides an economic offset for the first resource.

Referencing FIG. 93, an example transaction-enabling system 9300includes a machine 8502 having at least one of a compute taskrequirement, a networking task requirement, and/or an energy consumptiontask requirement. The example system 9300 further includes a controller8506 having a resource requirement circuit 8708 structured to determinean amount of a resource 8710 for the machine to service at least one ofthe compute task requirement, the networking task requirement, and/orthe energy consumption task requirement. The example controller 8506further includes a resource market circuit 9116 structured to access aresource market 9118, and a resource distribution circuit 8712structured to execute a transaction 8720 of the resource on the resourcemarket 9118 in response to the determined amount of the resource 8710.In certain embodiments, the resource distribution circuit 8712 isfurther structured to adaptively improve an output value of the machine,a cost of operation of the machine, and/or a resource utilization of themachine using executed transactions 8720 on the resource market 9118.Example and non-limiting resources include an energy resource, an energycredit resource, and/or a spectrum allocation resource. An exampleresource market 9118 includes a spot market and/or a forward market forone or more resources. Example and non-limiting operations of theresource distribution circuit 8712 to adaptively improve the outputvalue, cost of operation, and/or resource utilization of the machine8502 include: increasing an output volume, quantity, and/or quality;improving an output delivery time (e.g., an earlier delivery and/or asynchronized delivery with some other process of the system); reducing atotal resource utilization; reducing a resource utilization for aparticular type of resource; reducing a resource consumed per outputelement produced of the machine; and/or time-shifting resourceconsumption from a less efficient (e.g., based on cost, capacity,availability, and/or impact of the resource utilization) time frame to amore efficient time frame for the resource consumption. Example andnon-limiting operations of the resource distribution circuit 8712include adjusting a consumed resource of one type to a consumed resourceof another type by the machine(s), for example by substituting a secondresource for a first resource, and/or by substituting a second task fora first task, thereby substituting a second resource for a firstresource. Example and non-limiting operations of the resourcedistribution circuit 8712 include executing a transaction 8720 relatedto a second resource in response to a utilization of a first resource,for example where the second resource can be substituted for the firstresource, and/or where the second resource provides an economic offsetfor the first resource. An example resource distribution circuit 8712includes a machine learning component, an artificial intelligencecomponent, and/or a neural network component (e.g., as a continuousimprovement component 8722).

Referencing FIG. 94, an example procedure 9400 includes an operation9402 to determine an amount of a resource for a machine to service atleast one of a compute task requirement, a networking task requirement,and/or an energy consumption task requirement; an operation 9404 toaccess a resource market; and an operation 9406 to execute a transactionon the resource market in response to the amount of the resource. Incertain embodiments, an example procedure 9400 further includes anoperation 9408 to adaptively improve an output value, a cost ofoperation, and/or a resource utilization of the machine utilizing theexecuted transactions on the resource market.

Referencing FIG. 95, an example transaction-enabling system 9500includes a fleet of machines 8902 each having at least one of a computetask requirement, a networking task requirement, and/or an energyconsumption task requirement. The example system 9500 further includes acontroller 8506 having a resource requirement circuit 8708 structured todetermine an amount of a resource 8710 for each of the machines of thefleet of machines 8902 to service at least one of the compute taskrequirement, the networking task requirement, and/or the energyconsumption task requirement for each corresponding machine. The examplesystem 9500 further includes a resource market circuit 9116 structuredto access a resource market 9118, and a resource distribution circuit9112 structured to execute an aggregated transaction 8920 of theresource on the resource market 9118 in response to the determinedamount of the resource 8710 for each of the machines. The exampleresource market 9118 may include a spot market and/or a forward marketfor one or more of the resources. The example system 9500 includes theresource distribution circuit 9112 executing the aggregatedtransaction(s) 8920 to improve a resource performance 9502 of the fleetof machines 8902, where the resource performance 9502 includes one ormore of: a total consumption of one or more resources, a cost ofoperation of the fleet of machines 8902, and/or an aggregate outputvalue of the fleet of machines 8902 (e.g., including an output volume,quantity, and/or quality for a given amount of resources utilized,and/or an output volume, quantity, and/or quality per amount ofresources utilized). An example resource distribution circuit 9112 isfurther structured to adaptively improve the resource performance 9502of the fleet of machines 8902, and may further include a machinelearning component, an artificial intelligence component, and/or aneural network component (e.g., as a continuous improvement component8722).

Referencing FIG. 96, an example procedure 9600 includes an operation9602 to determine an amount of a resource, for each of machine of afleet of machines, to service at least one of a compute taskrequirement, a networking task requirement, and/or an energy consumptiontask requirement for each corresponding machine. The example procedure9600 further includes an operation 9604 to access a resource market, andan operation 9606 to execute a transaction on the resource market inresponse to an aggregated resource amount for the task requirements ofeach machine of the fleet of machines. In certain embodiments, theprocedure 9600 further includes an operation 9608 to adaptively improvean output value, a cost of operation, a resource utilization, and/or aresource performance of the fleet of machines, using executedtransactions on the market.

Referencing FIG. 97, an example transaction-enabling system 9700includes a machine 8502 having at least one of a compute taskrequirement, a networking task requirement, and/or an energy consumptiontask requirement. The example system 9700 further includes a controller8506 having a resource requirement circuit 8708 structured to determinean amount of a resource for the machine 8502 to service at least one ofthe compute task requirement, the networking task requirement, and/orthe energy consumption task requirement. The example controller 8506further includes a social media data circuit 9702 structured tointerpret data from a plurality of social media data sources 9704; aforward resource market circuit 8716 structured to access a forwardresource market 8718; a market forecasting circuit 9706 structured topredict a forward market price 9708 of the resource on the forwardresource market 8718 in response to the plurality of social media datasources 9704; and a resource distribution circuit 8712 structured toexecute a transaction 8720 of the resource on the forward resourcemarket 8718 in response to the determined amount of the resource 8710and the predicted forward market price 9708 of the resource. Example andnon-limiting resources include a spectrum allocation resource, an energyresource, and/or an energy credit resource. Example and non-limitingtransactions 8720 include buying and/or selling one or more of theresources. An example market forecasting circuit 9706 adaptivelyimproves the forward market price 9708 prediction, for example utilizinga machine learning component, an artificial intelligence component,and/or a neural network component (e.g., as a continuous improvementcomponent 9710). An example resource distribution circuit 8712 isfurther structured to adaptively improve an operating aspect of themachine 8502, for example output value of the machine, a cost ofoperation of the machine, a resource utilization of the machine, and/ora resource performance of the machine using executed transactions 8720on the forward resource market 8718. In a further example, the resourcedistribution circuit 8712 further includes a machine learning component,an artificial intelligence component, and/or a neural network component(e.g., as a continuous improvement component 8722).

Referencing FIG. 98, an example procedure 9800 includes an operation9802 to determine an amount of a resource for a machine to service atleast one of a compute task requirement, a networking task requirement,and/or an energy consumption task requirement; an operation 9804 tointerpret data from a plurality of social media data sources; anoperation 9806 to access a forward resource market; an operation 9808 topredict a forward market price of the resource on the forward resourcemarket in response to the plurality of social media data sources; and anoperation 9810 to execute a transaction of the resource on the forwardresource market in response to the determined amount of the resource andthe predicted forward market price of the resource. An example procedure9800 further includes an operation 9812 to adaptively improve an outputvalue and/or a cost of operation of the machine using executedtransactions on the market. In certain embodiments, the procedure 9600further includes an operation to adaptively improve the operation 9808to predict the forward market price of the resource. In certainembodiments, the operation 9812 includes an operation to adaptivelyimprove a resource utilization and/or a resource performance of themachine using the executed transactions on the market.

Referencing FIG. 99, an example transaction-enabling system 9900includes a machine 8502 having at least one of a compute taskrequirement, a networking task requirement, and/or an energy consumptiontask requirement. The example system 9900 further includes a controller8506 having a resource requirement circuit 8708 structured to determinean amount of a resource 8710 for the machine 8502 to service at leastone of the compute task requirement, the networking task requirement,and/or the energy consumption task requirement, and a resource marketcircuit 9902 structured to access a resource market 9904. The examplecontroller 8506 further includes a market testing circuit 9906structured to execute a first transaction 8720 of the resource on theresource market 9904 in response to the determined amount of theresource 8710; and an arbitrage execution circuit 9908 structured toexecute a second transaction 8720 of the resource on the resource market9904 in response to the determined amount of the resource and further inresponse to an outcome 9914 of the execution of the first transaction,wherein the second transaction comprises a larger transaction than thefirst transaction (e.g., according to the first transaction parameters9910 defining a lower transaction volume, amount, or cost than thesecond transaction parameters 9912). The resource may be any type ofresource as set forth throughout the present disclosure. An examplearbitrage execution circuit 9908 is further structured to adaptivelyimprove an arbitrage parameter 9916 by adjusting the first transactionparameter 9910 and/or the second transaction parameter 9912. Example andnon-limiting adjustments to the transaction parameters 9910, 9912include adjustments such as: adjusting a relative size of the firsttransaction and the second transaction; adjusting a resource type of thefirst transaction and the second transaction; adjusting a selectedresource market 9904 of the first transaction and/or the secondtransaction; adjusting a forward market time-frame and/or a spot marketselection of the first transaction parameter 9910 and/or the secondtransaction parameter 9912; adjusting a timing between the firsttransaction and the second transaction; adjusting a time to initiate thefirst transaction (e.g., a time of day; a calendar date; a relative dateto a week beginning, holiday, economic data report, etc.); and/orchanging a number of transactions such as a third, fourth, or fifthtransactions, including determining transaction parameters 9910, 9912for each transaction, a trajectory of volumes and timing for eachtransaction, and further determining or adjusting parameters for eachsubsequent transaction based on the transaction outcome of eachpreceding transaction (or a subset of the preceding transactions, or allof the preceding transactions). In certain embodiments, the arbitrageexecution circuit 9908 may include a continuous improvement component,such as a machine learning component, an artificial intelligencecomponent, and/or a neural network component. In certain embodiments,the arbitrage parameter 9916 may include one or more parameters such as:a similarity value in a market response of the first transaction and thesecond transaction; a confidence value of the first transaction toprovide test information for the second transaction; and/or a marketeffect of the first transaction.

Referencing FIG. 100, an example procedure 10000 includes an operation10002 to determine an amount of a resource to service a task requirementof a machine, an operation 10004 to access a resource market, anoperation 10006 to execute a first transaction on the resource market,and an operation 10008 to execute a second, larger transaction on theresource market. In certain embodiments, the procedure 10000 furtherincludes an operation 10010 to adaptively improve one of an outputvalue, a cost of operation, a resource utilization, and/or a resourceperformance of the machine by adjusting parameters of the first and/orsecond transaction.

Referencing FIG. 101, an example apparatus 10100 includes a resourcerequirement circuit 8708 structured to determine an amount of a resource8710 for a machine to service at least one of a compute taskrequirement, a networking task requirement, and/or an energy consumptiontask requirement. The example apparatus 10100 further includes aresource market circuit 9902 structured to access a resource market, forexample to establish communications with and/or to communicatetransaction parameters (buying, selling, amounts, prices, types ofpricing limits, etc.) with the market(s) for the resource(s). Theexample apparatus 10100 further includes a market testing circuit 9906structured to execute a first transaction 10102 of the resource on theresource market 9904 in response to the determined amount of theresource 8710; and an arbitrage execution circuit 9908 structured toexecute a second transaction 10106 of the resource on the resourcemarket 9904 in response to the determined amount of the resource andfurther in response to an outcome 10104 of the execution of the firsttransaction, wherein the second transaction 10106 comprises a largertransaction than the first transaction 10102 (e.g., according to thefirst transaction parameters 9910 defining a lower transaction volume,amount, or cost than the second transaction parameters 9912). Theresource may be any type of resource as set forth throughout the presentdisclosure. An example arbitrage execution circuit 9908 is furtherstructured to adaptively improve an arbitrage parameter 9916 byadjusting the first transaction parameter 9910 and/or the secondtransaction parameter 9912. In certain embodiments, the arbitrageexecution circuit 9908 may include and/or communicate with a continuousimprovement component 10110, such as a machine learning component, an AIcomponent, and/or a neural network component. Example and non-limitingadjustments to the transaction parameters 9910, 9912 include adjustmentssuch as: adjusting a relative size of the first transaction 10102 andthe second transaction 10106; adjusting a resource type of the firsttransaction 10102 and the second transaction 10106; adjusting a selectedmarket of the first transaction 10102 and/or the second transaction10106 (e.g., where more than one market is available); adjusting aforward market time-frame and/or a spot market selection of the firsttransaction parameter 9910 and/or the second transaction parameter 9912;adjusting a timing between the first transaction 10102 and the secondtransaction 10106; adjusting a time to initiate the first transaction10102 (e.g., a time of day; a calendar date; a relative date to a weekbeginning, holiday, economic data report, etc.); and/or changing anumber of transactions such as a third, fourth, or fifth transactions(e.g., additional transactions 10108), including determining parameters9910, 9912 for each transaction, a trajectory of volumes and timing foreach transaction, and further determining or adjusting parameters foreach subsequent transaction based on the transaction outcome of eachpreceding transaction (or a subset of the preceding transactions, or allof the preceding transactions). In certain embodiments, the arbitrageparameter 9916 may include one or more parameters such as: a similarityvalue in a market response of the first transaction and the secondtransaction; a confidence value of the first transaction to provide testinformation for the second transaction; and/or a market effect of thefirst transaction.

Referencing FIG. 102, an example transaction-enabling system 10200includes a machine 8502 having an associated resource capacity 10202 fora resource, the machine 8502 having a requirement for at least one of acore task, a compute task, an energy storage task, a data storage task,and/or a networking task. The example system 10200 further includes acontroller 8506 having a resource requirement circuit 10204 structuredto determine an amount of the resource 8710 to service the requirementof the at least one of the core task, the compute task, the energystorage task, the data storage task, and/or the networking task inresponse to the requirement of the at least one of the core task, thecompute task, the energy storage task, the data storage task, and/or thenetworking task. The example controller 8506 further includes a resourcedistribution circuit 10206 structured to adaptively improve, in responseto the associated resource capacity 10202, a resource delivery 10208 ofthe resource between the core task, the compute task, the energy storagetask, the data storage task, and/or the networking task. An exampleassociated resource capacity 10202 includes one or more of: a computecapacity for a compute resource; an energy capacity for an energyresource (including at least power, storage, regeneration, torque,and/or credits); a network bandwidth capacity for a networking resource;and/or a data storage capacity for a data storage resource. In certainembodiments, the resource distribution circuit 10206 provides commandsto the machine 8502, and/or provides resource delivery commands 10210 tothe associated resource capacity 10202 to implement the determinationand/or improvement of the resource delivery 10208. In certainembodiments, the controller 8506 determines one or more of a machineoutput value 10212, a machine resource utilization 10214, a machineresource performance 10216, and/or a machine cost of operation 10218,and the resource distribution circuit 10206 further adaptively improvesthe resource delivery 10208 in response to one or more of these. Anexample resource distribution circuit 10206 includes a continuousimprovement circuit 10220, which may include one or more of a machinelearning component, an AI component, and/or a neural network component.

Referencing FIG. 103, an example procedure 10300 includes an operation10302 to determine an amount of a resource to service one or more tasksof a machine, an operation 10304 to determine a resource requirement forthe machine based on the amount of the resource to service the one ormore tasks, and an operation 10306 to adaptively improve, in response toan associated resource capacity of the machine, a resource deliverybetween the tasks of the machine.

Referencing FIG. 104, an example transaction-enabling system 10400includes a fleet of machines 10402 each having an associated resourcecapacity 10404 for a resource, and each machine of the fleet of machines10402 further having a requirement for at least one of a core task, acompute task, an energy storage task, a data storage task, and anetworking task. The example system 10400 further includes a controller8506 having a resource requirement circuit 10204 structured to determinean amount of the resource 8710 to service the requirement of the atleast one of the core task, the compute task, the energy storage task,the data storage task, and/or the networking task for each machine ofthe fleet of machines 10402 in response to the requirement of the atleast one of the core task, the compute task, the energy storage task,the data storage task, and/or the networking task. The examplecontroller 8506 further includes a resource distribution circuit 10206structured to adaptively improve, in response to the associated resourcecapacity 10404, an aggregated resource delivery 10408 of the resourcebetween the core task, the compute task, the energy storage task, thedata storage task, and/or the networking task. An example associatedresource capacity 10404 includes one or more of: a compute capacity fora compute resource; an energy capacity for an energy resource (includingat least power, storage, regeneration, torque, and/or credits); anetwork bandwidth capacity for a networking resource; and/or a datastorage capacity for a data storage resource. In certain embodiments,the resource distribution circuit 10206 provides commands to the fleetof machines 10402, and/or provides resource delivery commands 10210 tothe associated resource capacity 10404 to implement the determinationand/or improvement of the aggregated resource delivery 10408. In certainembodiments, the controller 8506 determines one or more of a fleetoutput value 10412, a fleet resource utilization 10414, a fleet resourceperformance 10416, and/or a fleet cost of operation 10418, and theresource distribution circuit 10206 further adaptively improves theaggregated resource delivery 10408 in response to one or more of these.An example resource distribution circuit 10206 includes a continuousimprovement circuit 10220, which may include one or more of a machinelearning component, an AI component, and/or a neural network component.

An example resource distribution circuit 10206 is further structured tointerpret a resource transferability value 10420 between at least twomachines of the fleet of machines, and to adaptively improve theaggregated resource delivery 10408 further in response to the resourcetransferability value 10420. Example and non-limiting resourcetransferability values 10420 include one or more of: an ability tosubstitute or distribute tasks at least partially between machines ofthe fleet of machines 10402; an ability to transfer resources from theassociated resource capacities between machines of the fleet of machines10402; and/or an ability to substitute a first resource for a secondresource within a machine or between machines of the fleet of machines10402. A substitution of resources may further include consideration ofa rate of resource substitution (e.g., resource consumption per unittime), a capacity of a resource, positive and negative resource flows(e.g., consumption, regeneration, or acquisition of a resource), and/ortime frames for the resource substitution (e.g., transferring a resourcefrom a first machine to a second machine at a first time, andtransferring from the second machine to the first machine or anothermachine at a second time).

Referencing FIG. 105, an example procedure 10500 includes an operation10502 to determine an aggregated amount of a resource to service a coretask, a compute task, an energy storage task, a data storage task,and/or a networking task for each machine of a fleet of machines, and anoperation 10504 to determine a resource requirement including at leastone of a core task requirement, a compute task requirement, an energystorage task requirement, a data storage task requirement, and/or anetworking task requirement for each machine of the fleet of machines.The example procedure 10500 further includes an operation 10506 toadaptively improve, in response to an aggregated associated resourcecapacity of the fleet of machines, an aggregated resource delivery ofthe resource between the core task, the compute task, the energy storagetask, the data storage task, and/or the networking task for each machineof the fleet of machines.

Referencing FIG. 106, an example apparatus 10600 includes a controller8506 having resource requirement circuit 10204 structured to determinean aggregated amount of a resource 8710 to service a core task, acompute task, an energy storage task, a data storage task, and/or anetworking task for each machine of a fleet of machines in response toat least one of a core task requirement, a compute task requirement, anenergy storage task requirement, a data storage task requirement, and/ora networking task requirement for each machine of the fleet of machines;and a resource distribution circuit 10206 structured to adaptivelyimprove, in response to an aggregated associated resource capacity 10604of the fleet of machines, an aggregated resource delivery 10408 of theresource between the core task, the compute task, the energy storagetask, the data storage task, and/or the networking task for each machineof the fleet of machines. Example associated resource capacities 10604include one or more of: a compute capacity for a compute resource; anenergy capacity for an energy resource (including at least power,storage, regeneration, torque, and/or credits); a network bandwidthcapacity for a networking resource; and/or a data storage capacity for adata storage resource. In certain embodiments, the resource distributioncircuit 10206 provides commands to the fleet of machines 10402, and/orprovides resource delivery commands 10210 to the associated resourcecapacity elements to implement the determination and/or improvement ofthe aggregated resource delivery 10408. In certain embodiments, thecontroller 8506 determines one or more of a fleet output value 10412, afleet resource utilization 10414, a fleet resource performance 10416,and/or a fleet cost of operation 10418, and the resource distributioncircuit 10206 further adaptively improves the aggregated resourcedelivery 10408 in response to one or more of these. An example resourcedistribution circuit 10206 includes a continuous improvement circuit10220, which may include one or more of a machine learning component, anAI component, and/or a neural network component.

Referencing FIG. 107, an example transaction-enabling system 10700includes a machine 8502 having at least one task requirement, andcontroller 8506 having a resource requirement circuit 8708 structured todetermine an amount of resource(s) 8710 for the machine 8502 to servicethe task requirement(s). The example controller 8506 further includes aforward resource market circuit 10702 structured to access a forwardresource market (resource market(s) 10704), and a resource marketcircuit 10706 structured to access a resource market 10704 (e.g., a spotmarket, or a different forward market). The example controller 8506further includes a resource distribution circuit 10708 structured toexecute a transaction 10710 on the forward resource market and/or theresource market in response to the determined amount of the resource8710. An example resource distribution circuit 10708 is furtherstructured to adaptively improve at least one of: an output of themachine, a cost of operation of the machine, a resource utilization ofthe machine, and/or a resource performance of the machine using thetransaction(s) 10710. In certain embodiments, the resource distributioncircuit 10708 includes continuous improvement component, such as amachine learning component, an AI component, and/or a neural networkcomponent, to perform the adaptive improvement. In certain embodiments,the resource distribution circuit 10708 may determine a second amount ofa second resource for the machine to service the task requirement(s),and may further execute a transaction 10710 of the first resource and/orthe second resource to improve an aspect of the machine operation. Thesecond resource may be a substitute resource for the first resource,and/or may be a corresponding resource to the first resource—for examplea resource that is expected to make a corresponding or countering pricemove relative to the first resource. In certain embodiments, the forwardresource market is a forward market for a first time scale (e.g., onemonth out) and the resource market is a forward market for a second timescale (e.g., two months out). In certain embodiments, the forward marketis a forward market for the first resource or the second resource, andthe resource market is a spot market either the first resource or thesecond resource (e.g., the same resource as for the forward market, orthe other one of the first or second resource), and/or the resourcemarket is a forward market for either the first resource or the secondresource (e.g., the same resource as for the forward market but at adifferent time scale, or the other one of the first or second resourceat the same or a different time scale). In certain embodiments, thetransaction(s) 10710 may be one of: a sale of a resource, a purchase ofa resource, a short sale of a resource, a call option for a resource, aput option for a resource, and/or any of the foregoing with relation toa substitute resource or a correlated resource. An example resourcedistribution circuit 10708 executes the transaction(s) 10710 using asubstitute resource and/or a correlated resource as a substitute for atransaction of the to-be-traded resource. An example resourcedistribution circuit 10708 executes the transaction(s) 10710 using asubstitute resource and/or a correlated resource as an economic offsetof the to-be-traded resource. An example resource distribution circuit10708 executes the transaction(s) 10710 using a substitute resourceand/or a correlated resource in concert with a transaction of theto-be-traded resource.

Referencing FIG. 108, an example procedure 10800 includes an operation10802 to determine a resource requirement for task(s) of a machine, andan operation 10804 to access a forward resource market for the resource,a substitute resource, and/or a correlated resource. The exampleprocedure 10800 further includes an operation 10806 to access a resourcemarket for the resource, a substitute resource, and/or a correlatedresource. The resource market may be a spot market or a forward market.The example procedure 10800 further includes an operation 10808 toexecute a transaction on the forward resource market and/or the resourcemarket in response to the resource requirements. In certain embodiments,the operation 10808 is performed to improve, and/or to adaptivelyimprove, an output value of the machine, a cost of operation of themachine, a resource utilization of the machine, and/or a resourceperformance of the machine.

Referencing FIG. 109, an example procedure 10900 includes an operation10902 to determine a second resource that can substitute for a firstresource, and/or that is correlated with the first resource and furtherincludes an operation 10904 that determines that the second resource isa functionally equivalent, operationally acceptable alternative to thefirst resource or determines the second resource as an economic offsetresource. Without limitation to any other aspect of the presentdisclosure, a correlated resource includes any resource that is expectedto make a correlated price move (e.g., either with the first resource oropposite the first resource), and/or any resource that is expected tohave correlated availability for purchase with the first resource (e.g.,either availability that moves with the first resource or opposite thefirst resource). The example procedure 10900 further includes anoperation 10906 to consider (e.g., to predict and/or adaptively predictusing a machine learning, AI, or neural network component) a forwardmarket price of the first resource and/or the second resource, and anoperation 10908 to execute a transaction on a market (e.g., a spotmarket and/or a forward market) of the first resource and/or the secondresource.

In embodiments, provided herein is a transaction-enabling system havinga machine with a regenerative energy facility that optimizes allocationof delivery of energy produced among compute tasks, networking tasks andenergy consumption tasks. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga machine that automatically purchases its energy in a forward marketfor energy. In embodiments, provided herein is a transaction-enablingsystem having a machine with a regenerative energy facility thatoptimizes allocation of delivery of energy produced among compute tasks,networking tasks and energy consumption tasks and having a machine thatautomatically purchases energy credits in a forward market. Inembodiments, provided herein is a transaction-enabling system having amachine with a regenerative energy facility that optimizes allocation ofdelivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy. Inembodiments, provided herein is a transaction-enabling system having amachine with a regenerative energy facility that optimizes allocation ofdelivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward market.In embodiments, provided herein is a transaction-enabling system havinga machine with a regenerative energy facility that optimizes allocationof delivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrum.In embodiments, provided herein is a transaction-enabling system havinga machine with a regenerative energy facility that optimizes allocationof delivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having a machine that automatically sellsits compute capacity on a forward market for compute capacity. Inembodiments, provided herein is a transaction-enabling system having amachine with a regenerative energy facility that optimizes allocation ofdelivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having a machine that automatically sellsits compute storage capacity on a forward market for storage capacity.In embodiments, provided herein is a transaction-enabling system havinga machine with a regenerative energy facility that optimizes allocationof delivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity. In embodiments, provided herein is a transaction-enablingsystem having a machine with a regenerative energy facility thatoptimizes allocation of delivery of energy produced among compute tasks,networking tasks and energy consumption tasks and having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga fleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum. In embodiments, provided herein isa transaction-enabling system having a machine with a regenerativeenergy facility that optimizes allocation of delivery of energy producedamong compute tasks, networking tasks and energy consumption tasks andhaving a fleet of machines that automatically optimize energyutilization for compute task allocation. In embodiments, provided hereinis a transaction-enabling system having a machine with a regenerativeenergy facility that optimizes allocation of delivery of energy producedamong compute tasks, networking tasks and energy consumption tasks andhaving a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy. Inembodiments, provided herein is a transaction-enabling system having amachine with a regenerative energy facility that optimizes allocation ofdelivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum. Inembodiments, provided herein is a transaction-enabling system having amachine with a regenerative energy facility that optimizes allocation ofdelivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga machine that automatically purchases its energy in a spot market forenergy. In embodiments, provided herein is a transaction-enabling systemhaving a machine with a regenerative energy facility that optimizesallocation of delivery of energy produced among compute tasks,networking tasks and energy consumption tasks and having a machine thatautomatically purchases energy credits in a spot market. In embodiments,provided herein is a transaction-enabling system having a machine with aregenerative energy facility that optimizes allocation of delivery ofenergy produced among compute tasks, networking tasks and energyconsumption tasks and having a fleet of machines that automaticallyaggregate purchasing in a spot market for energy. In embodiments,provided herein is a transaction-enabling system having a machine with aregenerative energy facility that optimizes allocation of delivery ofenergy produced among compute tasks, networking tasks and energyconsumption tasks and having a fleet of machines that automaticallyaggregate purchasing energy credits in a spot market. In embodiments,provided herein is a transaction-enabling system having a machine with aregenerative energy facility that optimizes allocation of delivery ofenergy produced among compute tasks, networking tasks and energyconsumption tasks and having a machine that automatically purchasesspectrum allocation in a spot market for network spectrum. Inembodiments, provided herein is a transaction-enabling system having amachine with a regenerative energy facility that optimizes allocation ofdelivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum. In embodiments, provided herein is a transaction-enablingsystem having a machine with a regenerative energy facility thatoptimizes allocation of delivery of energy produced among compute tasks,networking tasks and energy consumption tasks and having a fleet ofmachines that automatically optimize energy utilization for compute taskallocation. In embodiments, provided herein is a transaction-enablingsystem having a machine with a regenerative energy facility thatoptimizes allocation of delivery of energy produced among compute tasks,networking tasks and energy consumption tasks and having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga fleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy credits. In embodiments,provided herein is a transaction-enabling system having a machine with aregenerative energy facility that optimizes allocation of delivery ofenergy produced among compute tasks, networking tasks and energyconsumption tasks and having a fleet of machines that automaticallyaggregate data on collective optimization of spot market purchases ofnetwork spectrum. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga fleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity. In embodiments,provided herein is a transaction-enabling system having a machine with aregenerative energy facility that optimizes allocation of delivery ofenergy produced among compute tasks, networking tasks and energyconsumption tasks and having a fleet of machines that automatically selltheir aggregate compute storage capacity on a forward market for storagecapacity. In embodiments, provided herein is a transaction-enablingsystem having a machine with a regenerative energy facility thatoptimizes allocation of delivery of energy produced among compute tasks,networking tasks and energy consumption tasks and having a fleet ofmachines that automatically sell their aggregate energy storage capacityon a forward market for energy storage capacity. In embodiments,provided herein is a transaction-enabling system having a machine with aregenerative energy facility that optimizes allocation of delivery ofenergy produced among compute tasks, networking tasks and energyconsumption tasks and having a fleet of machines that automatically selltheir aggregate network bandwidth on a forward market for networkcapacity. In embodiments, provided herein is a transaction-enablingsystem having a machine with a regenerative energy facility thatoptimizes allocation of delivery of energy produced among compute tasks,networking tasks and energy consumption tasks and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a machine with aregenerative energy facility that optimizes allocation of delivery ofenergy produced among compute tasks, networking tasks and energyconsumption tasks and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from social media data sources. In embodiments, providedherein is a transaction-enabling system having a machine with aregenerative energy facility that optimizes allocation of delivery ofenergy produced among compute tasks, networking tasks and energyconsumption tasks and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom social media data sources. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga machine that automatically forecasts forward market value of computecapability based on information collected from social media datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine with a regenerative energy facility thatoptimizes allocation of delivery of energy produced among compute tasks,networking tasks and energy consumption tasks and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a machine with a regenerativeenergy facility that optimizes allocation of delivery of energy producedamong compute tasks, networking tasks and energy consumption tasks andhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of energy storage capacity by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having amachine with a regenerative energy facility that optimizes allocation ofdelivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of network spectrumor bandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a machine with a regenerativeenergy facility that optimizes allocation of delivery of energy producedamong compute tasks, networking tasks and energy consumption tasks andhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of energy credits by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having amachine with a regenerative energy facility that optimizes allocation ofdelivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having amachine with a regenerative energy facility that optimizes allocation ofdelivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having amachine with a regenerative energy facility that optimizes allocation ofdelivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having amachine with a regenerative energy facility that optimizes allocation ofdelivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga fleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task. In embodiments, provided hereinis a transaction-enabling system having a machine with a regenerativeenergy facility that optimizes allocation of delivery of energy producedamong compute tasks, networking tasks and energy consumption tasks andhaving a fleet of machines that automatically allocate collectivenetworking capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a machine with aregenerative energy facility that optimizes allocation of delivery ofenergy produced among compute tasks, networking tasks and energyconsumption tasks and having a smart contract wrapper using adistributed ledger wherein the smart contract embeds IP licensing termsfor intellectual property embedded in the distributed ledger and whereinexecuting an operation on the distributed ledger provides access to theintellectual property and commits the executing party to the IPlicensing terms. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga distributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to an aggregatestack of intellectual property. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga distributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to agree to anapportionment of royalties among the parties in the ledger. Inembodiments, provided herein is a transaction-enabling system having amachine with a regenerative energy facility that optimizes allocation ofdelivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property. Inembodiments, provided herein is a transaction-enabling system having amachine with a regenerative energy facility that optimizes allocation ofdelivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to commit aparty to a contract term. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga distributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga distributed ledger that tokenizes executable algorithmic logic, suchthat operation on the distributed ledger provides provable access to theexecutable algorithmic logic. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga distributed ledger that tokenizes a 3D printer instruction set, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga distributed ledger that tokenizes an instruction set for a coatingprocess, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga distributed ledger that tokenizes an instruction set for asemiconductor fabrication process, such that operation on thedistributed ledger provides provable access to the fabrication process.In embodiments, provided herein is a transaction-enabling system havinga machine with a regenerative energy facility that optimizes allocationof delivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having a distributed ledger that tokenizesa firmware program, such that operation on the distributed ledgerprovides provable access to the firmware program. In embodiments,provided herein is a transaction-enabling system having a machine with aregenerative energy facility that optimizes allocation of delivery ofenergy produced among compute tasks, networking tasks and energyconsumption tasks and having a distributed ledger that tokenizes aninstruction set for an FPGA, such that operation on the distributedledger provides provable access to the FPGA. In embodiments, providedherein is a transaction-enabling system having a machine with aregenerative energy facility that optimizes allocation of delivery ofenergy produced among compute tasks, networking tasks and energyconsumption tasks and having a distributed ledger that tokenizesserverless code logic, such that operation on the distributed ledgerprovides provable access to the serverless code logic. In embodiments,provided herein is a transaction-enabling system having a machine with aregenerative energy facility that optimizes allocation of delivery ofenergy produced among compute tasks, networking tasks and energyconsumption tasks and having a distributed ledger that tokenizes aninstruction set for a crystal fabrication system, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga machine with a regenerative energy facility that optimizes allocationof delivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having a distributed ledger that tokenizesan instruction set for a food preparation process, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a machine with a regenerative energy facility that optimizesallocation of delivery of energy produced among compute tasks,networking tasks and energy consumption tasks and having a distributedledger that tokenizes an instruction set for a polymer productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga distributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine with aregenerative energy facility that optimizes allocation of delivery ofenergy produced among compute tasks, networking tasks and energyconsumption tasks and having a distributed ledger that tokenizes aninstruction set for a biological production process, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a machine with a regenerative energy facility that optimizesallocation of delivery of energy produced among compute tasks,networking tasks and energy consumption tasks and having a distributedledger that tokenizes a trade secret with an expert wrapper, such thatoperation on the distributed ledger provides provable access to thetrade secret and the wrapper provides validation of the trade secret bythe expert. In embodiments, provided herein is a transaction-enablingsystem having a machine with a regenerative energy facility thatoptimizes allocation of delivery of energy produced among compute tasks,networking tasks and energy consumption tasks and having a distributedledger that aggregates views of a trade secret into a chain that proveswhich and how many parties have viewed the trade secret. In embodiments,provided herein is a transaction-enabling system having a machine with aregenerative energy facility that optimizes allocation of delivery ofenergy produced among compute tasks, networking tasks and energyconsumption tasks and having a distributed ledger that tokenizes aninstruction set, such that operation on the distributed ledger providesprovable access to the instruction set and execution of the instructionset on a system results in recording a transaction in the distributedledger. In embodiments, provided herein is a transaction-enabling systemhaving a machine with a regenerative energy facility that optimizesallocation of delivery of energy produced among compute tasks,networking tasks and energy consumption tasks and having a distributedledger that tokenizes an item of intellectual property and a reportingsystem that reports an analytic result based on the operations performedon the distributed ledger or the intellectual property. In embodiments,provided herein is a transaction-enabling system having a machine with aregenerative energy facility that optimizes allocation of delivery ofenergy produced among compute tasks, networking tasks and energyconsumption tasks and having a distributed ledger that aggregates a setof instructions, where an operation on the distributed ledger adds atleast one instruction to a pre-existing set of instructions to provide amodified set of instructions. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga smart wrapper for management of a distributed ledger that aggregatessets of instructions, where the smart wrapper manages allocation ofinstruction sub-sets to the distributed ledger and access to theinstruction sub-sets. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga smart wrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga self-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment. In embodiments, provided herein is a transaction-enablingsystem having a machine with a regenerative energy facility thatoptimizes allocation of delivery of energy produced among compute tasks,networking tasks and energy consumption tasks and having an expertsystem that uses machine learning to optimize the execution ofcryptocurrency transactions based on tax status. In embodiments,provided herein is a transaction-enabling system having a machine with aregenerative energy facility that optimizes allocation of delivery ofenergy produced among compute tasks, networking tasks and energyconsumption tasks and having an expert system that aggregates regulatoryinformation covering cryptocurrency transactions and automaticallyselects a jurisdiction for an operation based on the regulatoryinformation. In embodiments, provided herein is a transaction-enablingsystem having a machine with a regenerative energy facility thatoptimizes allocation of delivery of energy produced among compute tasks,networking tasks and energy consumption tasks and having an expertsystem that uses machine learning to optimize the execution of acryptocurrency transaction based on real time energy price informationfor an available energy source. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havingan expert system that uses machine learning to optimize the execution ofa cryptocurrency transaction based on an understanding of availableenergy sources to power computing resources to execute the transaction.In embodiments, provided herein is a transaction-enabling system havinga machine with a regenerative energy facility that optimizes allocationof delivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having an expert system that uses machinelearning to optimize charging and recharging cycle of a rechargeablebattery system to provide energy for execution of a cryptocurrencytransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine with a regenerative energy facility thatoptimizes allocation of delivery of energy produced among compute tasks,networking tasks and energy consumption tasks and having an expertsystem that predicts a forward market price in a market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine with a regenerative energy facility that optimizes allocation ofdelivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havingan expert system that predicts a forward market price in a market basedon an understanding obtained by analyzing Internet of Things datasources and executes a cryptocurrency transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havingan expert system that predicts a forward market price in a market basedon an understanding obtained by analyzing social network data sourcesand executes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine with a regenerative energy facility thatoptimizes allocation of delivery of energy produced among compute tasks,networking tasks and energy consumption tasks and having an expertsystem that predicts a forward market price in an energy market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine with a regenerative energy facility that optimizes allocation ofdelivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine with aregenerative energy facility that optimizes allocation of delivery ofenergy produced among compute tasks, networking tasks and energyconsumption tasks and having an expert system that predicts a forwardmarket price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine with a regenerative energy facility that optimizes allocation ofdelivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine with a regenerative energy facility thatoptimizes allocation of delivery of energy produced among compute tasks,networking tasks and energy consumption tasks and having an expertsystem that predicts a forward market price in a market for computingresources based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine with a regenerative energy facility thatoptimizes allocation of delivery of energy produced among compute tasks,networking tasks and energy consumption tasks and having an expertsystem that predicts a forward market price in a market for advertisingbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine with a regenerative energy facility thatoptimizes allocation of delivery of energy produced among compute tasks,networking tasks and energy consumption tasks and having an expertsystem that predicts a forward market price in a market for advertisingbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine with a regenerative energy facility thatoptimizes allocation of delivery of energy produced among compute tasks,networking tasks and energy consumption tasks and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine with a regenerative energy facility that optimizes allocation ofdelivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine with a regenerative energy facility that optimizes allocation ofdelivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine with aregenerative energy facility that optimizes allocation of delivery ofenergy produced among compute tasks, networking tasks and energyconsumption tasks and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine with aregenerative energy facility that optimizes allocation of delivery ofenergy produced among compute tasks, networking tasks and energyconsumption tasks and having a machine that automatically forecastsforward market pricing of energy prices based on information collectedfrom business entity behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a machine with aregenerative energy facility that optimizes allocation of delivery ofenergy produced among compute tasks, networking tasks and energyconsumption tasks and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine with aregenerative energy facility that optimizes allocation of delivery ofenergy produced among compute tasks, networking tasks and energyconsumption tasks and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom business entity behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a machine with aregenerative energy facility that optimizes allocation of delivery ofenergy produced among compute tasks, networking tasks and energyconsumption tasks and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine with aregenerative energy facility that optimizes allocation of delivery ofenergy produced among compute tasks, networking tasks and energyconsumption tasks and having a machine that automatically forecastsforward market pricing of energy prices based on information collectedfrom human behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga machine that automatically forecasts forward market pricing of networkspectrum based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine with a regenerative energy facility thatoptimizes allocation of delivery of energy produced among compute tasks,networking tasks and energy consumption tasks and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine with a regenerative energy facility that optimizes allocation ofdelivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine with a regenerative energy facility that optimizes allocation ofdelivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing social data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine with a regenerative energy facility that optimizes allocation ofdelivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having an intelligent agent that isconfigured to solicit the attention resources of another externalintelligent agent. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga machine that automatically purchases attention resources in a forwardmarket for attention. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga fleet of machines that automatically aggregate purchasing in a forwardmarket for attention. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a machine with a regenerative energy facility thatoptimizes allocation of delivery of energy produced among compute tasks,networking tasks and energy consumption tasks and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga machine with a regenerative energy facility that optimizes allocationof delivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize provisioning and allocation of energy and compute resourcesto produce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize requisition andprovisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a machine with a regenerative energy facility thatoptimizes allocation of delivery of energy produced among compute tasks,networking tasks and energy consumption tasks and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having amachine with a regenerative energy facility that optimizes allocation ofdelivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having amachine with a regenerative energy facility that optimizes allocation ofdelivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto generate an indication that a current or prospective customer shouldbe contacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having amachine with a regenerative energy facility that optimizes allocation ofdelivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to at least one of aninput resource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a machine with aregenerative energy facility that optimizes allocation of delivery ofenergy produced among compute tasks, networking tasks and energyconsumption tasks and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having amachine with a regenerative energy facility that optimizes allocation ofdelivery of energy produced among compute tasks, networking tasks andenergy consumption tasks and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a machine with a regenerative energy facility thatoptimizes allocation of delivery of energy produced among compute tasks,networking tasks and energy consumption tasks and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having a machine with a regenerative energyfacility that optimizes allocation of delivery of energy produced amongcompute tasks, networking tasks and energy consumption tasks and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a utilization parameter for the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a machine with a regenerative energy facility thatoptimizes allocation of delivery of energy produced among compute tasks,networking tasks and energy consumption tasks and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases its energy in a forward marketfor energy. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aforward market for energy and having a machine that automaticallypurchases energy credits in a forward market. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases its energy in a forward market for energy andhaving a fleet of machines that automatically aggregate purchasing in aforward market for energy. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a fleetof machines that automatically aggregate purchasing energy credits in aforward market. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a machinethat automatically purchases spectrum allocation in a forward market fornetwork spectrum. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a machinethat automatically sells its compute capacity on a forward market forcompute capacity. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a machinethat automatically sells its compute storage capacity on a forwardmarket for storage capacity. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a machinethat automatically sells its energy storage capacity on a forward marketfor energy storage capacity. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a machinethat automatically sells its network bandwidth on a forward market fornetwork capacity. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a fleetof machines that automatically purchase spectrum allocation in a forwardmarket for network spectrum. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a fleetof machines that automatically optimize energy utilization for computetask allocation. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a fleetof machines that automatically aggregate data on collective optimizationof forward market purchases of energy. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a fleetof machines that automatically aggregate data on collective optimizationof forward market purchases of energy credits. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases its energy in a forward market for energy andhaving a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of network spectrum.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases its energy in a forward marketfor energy and having a fleet of machines that automatically aggregatedata on collective optimization of forward market sales of computecapacity. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aforward market for energy and having a machine that automaticallypurchases its energy in a spot market for energy. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a forward market for energy andhaving a machine that automatically purchases energy credits in a spotmarket. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically purchases its energy in a forwardmarket for energy and having a fleet of machines that automaticallyaggregate purchasing in a spot market for energy. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a forward market for energy andhaving a fleet of machines that automatically aggregate purchasingenergy credits in a spot market. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a machinethat automatically purchases spectrum allocation in a spot market fornetwork spectrum. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a fleetof machines that automatically purchase spectrum allocation in a spotmarket for network spectrum. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a fleetof machines that automatically optimize energy utilization for computetask allocation. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a fleetof machines that automatically aggregate data on collective optimizationof spot market purchases of energy. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a fleetof machines that automatically aggregate data on collective optimizationof spot market purchases of energy credits. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases its energy in a forward market for energy andhaving a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of network spectrum. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a forward market forenergy and having a fleet of machines that automatically sell theiraggregate compute capacity on a forward market for compute capacity. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a forward market forenergy and having a fleet of machines that automatically sell theiraggregate compute storage capacity on a forward market for storagecapacity. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aforward market for energy and having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a fleetof machines that automatically sell their aggregate network bandwidth ona forward market for network capacity. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a machinethat automatically forecasts forward market pricing of energy pricesbased on information collected from social media data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a forward market forenergy and having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from socialmedia data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from social media data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a forward market forenergy and having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from socialmedia data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a machinethat automatically executes an arbitrage strategy for purchase or saleof compute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a machinethat automatically executes an arbitrage strategy for purchase or saleof energy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases its energy in a forward market for energy andhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of network spectrum or bandwidth by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aforward market for energy and having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aforward market for energy and having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy credits bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aforward market for energy and having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a forward market forenergy and having a machine that automatically allocates its computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a machinethat automatically allocates its networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a fleetof machines that automatically allocate collective energy capacity amonga core task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a fleetof machines that automatically allocate collective compute capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a fleetof machines that automatically allocate collective networking capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a smartcontract wrapper using a distributed ledger wherein the smart contractembeds IP licensing terms for intellectual property embedded in thedistributed ledger and wherein executing an operation on the distributedledger provides access to the intellectual property and commits theexecuting party to the IP licensing terms. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases its energy in a forward market for energy andhaving a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toan aggregate stack of intellectual property. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases its energy in a forward market for energy andhaving a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toagree to an apportionment of royalties among the parties in the ledger.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases its energy in a forward marketfor energy and having a distributed ledger for aggregating intellectualproperty licensing terms, wherein a smart contract wrapper on thedistributed ledger allows an operation on the ledger to add intellectualproperty to an aggregate stack of intellectual property. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a forward market for energy andhaving a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to commit a party to a contractterm. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically purchases its energy in a forwardmarket for energy and having a distributed ledger that tokenizes aninstruction set, such that operation on the distributed ledger providesprovable access to the instruction set. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having adistributed ledger that tokenizes executable algorithmic logic, suchthat operation on the distributed ledger provides provable access to theexecutable algorithmic logic. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having adistributed ledger that tokenizes a 3D printer instruction set, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having adistributed ledger that tokenizes an instruction set for a coatingprocess, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having adistributed ledger that tokenizes an instruction set for a semiconductorfabrication process, such that operation on the distributed ledgerprovides provable access to the fabrication process. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a forward market for energy andhaving a distributed ledger that tokenizes a firmware program, such thatoperation on the distributed ledger provides provable access to thefirmware program. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having adistributed ledger that tokenizes an instruction set for an FPGA, suchthat operation on the distributed ledger provides provable access to theFPGA. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically purchases its energy in a forwardmarket for energy and having a distributed ledger that tokenizesserverless code logic, such that operation on the distributed ledgerprovides provable access to the serverless code logic. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a forward market for energy andhaving a distributed ledger that tokenizes an instruction set for acrystal fabrication system, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a forward market for energy andhaving a distributed ledger that tokenizes an instruction set for a foodpreparation process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a forward market for energy andhaving a distributed ledger that tokenizes an instruction set for apolymer production process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a forward market for energy andhaving a distributed ledger that tokenizes an instruction set forchemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a forward market for energy andhaving a distributed ledger that tokenizes an instruction set for abiological production process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a forward market for energy andhaving a distributed ledger that tokenizes a trade secret with an expertwrapper, such that operation on the distributed ledger provides provableaccess to the trade secret and the wrapper provides validation of thetrade secret by the expert. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a forward market forenergy and having a distributed ledger that tokenizes an instructionset, such that operation on the distributed ledger provides provableaccess to the instruction set and execution of the instruction set on asystem results in recording a transaction in the distributed ledger. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a forward market forenergy and having a distributed ledger that tokenizes an item ofintellectual property and a reporting system that reports an analyticresult based on the operations performed on the distributed ledger orthe intellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having adistributed ledger that aggregates a set of instructions, where anoperation on the distributed ledger adds at least one instruction to apre-existing set of instructions to provide a modified set ofinstructions. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aforward market for energy and having a smart wrapper for management of adistributed ledger that aggregates sets of instructions, where the smartwrapper manages allocation of instruction sub-sets to the distributedledger and access to the instruction sub-sets. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases its energy in a forward market for energy andhaving a smart wrapper for a cryptocurrency coin that directs executionof a transaction involving the coin to a geographic location based ontax treatment of at least one of the coin and the transaction in thegeographic location. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having aself-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aforward market for energy and having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aforward market for energy and having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having an expertsystem that uses machine learning to optimize the execution of acryptocurrency transaction based on real time energy price informationfor an available energy source. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having an expertsystem that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a forward market forenergy and having an expert system that uses machine learning tooptimize charging and recharging cycle of a rechargeable battery systemto provide energy for execution of a cryptocurrency transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a forward market forenergy and having an expert system that predicts a forward market pricein a market based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having an expertsystem that predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a forward market forenergy and having an expert system that predicts a forward market pricein a market based on an understanding obtained by analyzing Internet ofThings data sources and executes a cryptocurrency transaction based onthe forward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having an expertsystem that predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aforward market for energy and having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a forward market for energy andhaving an expert system that predicts a forward market price in anenergy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having an expertsystem that predicts a forward market price in a market for computingresources based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having an expertsystem that predicts a forward market price in a market for spectrum ornetwork bandwidth based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having an expertsystem that predicts a forward market price in a market for computingresources based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aforward market for energy and having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a forward market forenergy and having an expert system that predicts a forward market pricein a market for advertising based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a machinethat automatically forecasts forward market pricing of energy pricesbased on information collected from automated agent behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aforward market for energy and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a forward market forenergy and having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from automatedagent behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a machinethat automatically forecasts forward market value of compute capabilitybased on information collected from automated agent behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aforward market for energy and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a forward market for energy andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from business entity behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aforward market for energy and having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a forward market forenergy and having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from humanbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a machinethat automatically forecasts forward market pricing of network spectrumbased on information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a forward market forenergy and having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from humanbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a machinethat automatically forecasts forward market value of compute capabilitybased on information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a forward market forenergy and having an expert system that predicts a forward market pricein a market for spectrum or network bandwidth based on an understandingobtained by analyzing social data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having anintelligent agent that is configured to solicit the attention resourcesof another external intelligent agent. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a machinethat automatically purchases attention resources in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aforward market for energy and having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a forward market forenergy and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to predict alikelihood of a facility production outcome. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases its energy in a forward market for energy andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a facilityproduction outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases its energy in a forward marketfor energy and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize requisition andprovisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aforward market for energy and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aforward market for energy and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of inputresources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aforward market for energy and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aforward market for energy and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to an output parameter.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases its energy in a forward marketfor energy and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a forward market for energy and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases energy credits in a forwardmarket. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically purchases energy credits in aforward market and having a fleet of machines that automaticallyaggregate purchasing in a forward market for energy. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a forward market and having afleet of machines that automatically aggregate purchasing energy creditsin a forward market. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a machine thatautomatically purchases spectrum allocation in a forward market fornetwork spectrum. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a machine thatautomatically sells its compute capacity on a forward market for computecapacity. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aforward market and having a machine that automatically sells its computestorage capacity on a forward market for storage capacity. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a forward marketand having a machine that automatically sells its energy storagecapacity on a forward market for energy storage capacity. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a forward marketand having a machine that automatically sells its network bandwidth on aforward market for network capacity. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a fleet ofmachines that automatically purchase spectrum allocation in a forwardmarket for network spectrum. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a fleet ofmachines that automatically optimize energy utilization for compute taskallocation. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aforward market and having a fleet of machines that automaticallyaggregate data on collective optimization of forward market purchases ofenergy. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically purchases energy credits in aforward market and having a fleet of machines that automaticallyaggregate data on collective optimization of forward market purchases ofenergy credits. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of network spectrum. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a forward market and having afleet of machines that automatically aggregate data on collectiveoptimization of forward market sales of compute capacity. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a forward marketand having a machine that automatically purchases its energy in a spotmarket for energy. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a machine thatautomatically purchases energy credits in a spot market. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a forward market and having afleet of machines that automatically aggregate purchasing in a spotmarket for energy. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a fleet ofmachines that automatically aggregate purchasing energy credits in aspot market. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aforward market and having a machine that automatically purchasesspectrum allocation in a spot market for network spectrum. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a forward marketand having a fleet of machines that automatically purchase spectrumallocation in a spot market for network spectrum. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a forward market and having afleet of machines that automatically optimize energy utilization forcompute task allocation. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy credits. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of network spectrum. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a forward market and having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a forward market and having afleet of machines that automatically sell their aggregate computestorage capacity on a forward market for storage capacity. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a forward marketand having a fleet of machines that automatically sell their aggregateenergy storage capacity on a forward market for energy storage capacity.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases energy credits in a forwardmarket and having a fleet of machines that automatically sell theiraggregate network bandwidth on a forward market for network capacity. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a forward marketand having a machine that automatically forecasts forward market pricingof energy prices based on information collected from social media datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aforward market and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected fromsocial media data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a forward market and having amachine that automatically forecasts forward market value of computecapability based on information collected from social media datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aforward market and having a machine that automatically executes anarbitrage strategy for purchase or sale of compute capacity by testing aspot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aforward market and having a machine that automatically executes anarbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aforward market and having a machine that automatically executes anarbitrage strategy for purchase or sale of network spectrum or bandwidthby testing a spot market for compute capacity with a small transactionand rapidly executing a larger transaction based on the outcome of thesmall transaction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy credits by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking task.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases energy credits in a forwardmarket and having a machine that automatically allocates its computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a machine thatautomatically allocates its networking capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a fleet ofmachines that automatically allocate collective energy capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a fleet ofmachines that automatically allocate collective compute capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a fleet ofmachines that automatically allocate collective networking capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a smart contractwrapper using a distributed ledger wherein the smart contract embeds IPlicensing terms for intellectual property embedded in the distributedledger and wherein executing an operation on the distributed ledgerprovides access to the intellectual property and commits the executingparty to the IP licensing terms. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a distributedledger for aggregating intellectual property licensing terms, wherein asmart contract wrapper on the distributed ledger allows an operation onthe ledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a distributedledger for aggregating intellectual property licensing terms, wherein asmart contract wrapper on the distributed ledger allows an operation onthe ledger to add intellectual property to agree to an apportionment ofroyalties among the parties in the ledger. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a forward market and having adistributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to an aggregatestack of intellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a distributedledger for aggregating intellectual property licensing terms, wherein asmart contract wrapper on the distributed ledger allows an operation onthe ledger to commit a party to a contract term. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a forward market and having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a distributedledger that tokenizes executable algorithmic logic, such that operationon the distributed ledger provides provable access to the executablealgorithmic logic. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a distributedledger that tokenizes a 3D printer instruction set, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically purchases energy credits in aforward market and having a distributed ledger that tokenizes aninstruction set for a coating process, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a forward marketand having a distributed ledger that tokenizes an instruction set for asemiconductor fabrication process, such that operation on thedistributed ledger provides provable access to the fabrication process.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases energy credits in a forwardmarket and having a distributed ledger that tokenizes a firmwareprogram, such that operation on the distributed ledger provides provableaccess to the firmware program. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a distributedledger that tokenizes an instruction set for an FPGA, such thatoperation on the distributed ledger provides provable access to theFPGA. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically purchases energy credits in aforward market and having a distributed ledger that tokenizes serverlesscode logic, such that operation on the distributed ledger providesprovable access to the serverless code logic. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a forward market and having adistributed ledger that tokenizes an instruction set for a crystalfabrication system, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a forward market and having adistributed ledger that tokenizes an instruction set for a foodpreparation process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a forward market and having adistributed ledger that tokenizes an instruction set for a polymerproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a forward market and having adistributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a forward market and having adistributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a forward market and having adistributed ledger that tokenizes a trade secret with an expert wrapper,such that operation on the distributed ledger provides provable accessto the trade secret and the wrapper provides validation of the tradesecret by the expert. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a distributedledger that aggregates views of a trade secret into a chain that proveswhich and how many parties have viewed the trade secret. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a forward market and having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a forward market and having adistributed ledger that tokenizes an item of intellectual property and areporting system that reports an analytic result based on the operationsperformed on the distributed ledger or the intellectual property. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a forward marketand having a distributed ledger that aggregates a set of instructions,where an operation on the distributed ledger adds at least oneinstruction to a pre-existing set of instructions to provide a modifiedset of instructions. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a smart wrapperfor management of a distributed ledger that aggregates sets ofinstructions, where the smart wrapper manages allocation of instructionsub-sets to the distributed ledger and access to the instructionsub-sets. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aforward market and having a smart wrapper for a cryptocurrency coin thatdirects execution of a transaction involving the coin to a geographiclocation based on tax treatment of at least one of the coin and thetransaction in the geographic location. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a forward marketand having an expert system that uses machine learning to optimize theexecution of cryptocurrency transactions based on tax status. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a forward marketand having an expert system that aggregates regulatory informationcovering cryptocurrency transactions and automatically selects ajurisdiction for an operation based on the regulatory information. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a forward marketand having an expert system that uses machine learning to optimize theexecution of a cryptocurrency transaction based on real time energyprice information for an available energy source. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a forward market and having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a forward marketand having an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a forward marketand having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a forward marketand having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a cryptocurrency transaction based onthe forward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aforward market and having an expert system that predicts a forwardmarket price in an energy market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having an expert systemthat predicts a forward market price in an energy market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a forward marketand having an expert system that predicts a forward market price in amarket for computing resources based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having an expert systemthat predicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having an expert systemthat predicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aforward market and having an expert system that predicts a forwardmarket price in a market for advertising based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a forward market and having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a forward marketand having a machine that automatically forecasts forward market pricingof network spectrum based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases energy credits in a forwardmarket and having a machine that automatically forecasts forward marketvalue of compute capability based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a forward marketand having a machine that automatically forecasts forward market pricingof network spectrum based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases energy credits in a forwardmarket and having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from businessentity behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a forward marketand having a machine that automatically forecasts forward market pricingof network spectrum based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aforward market and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a forward marketand having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing social data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having an intelligentagent that is configured to solicit the attention resources of anotherexternal intelligent agent. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a machine thatautomatically purchases attention resources in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aforward market and having a fleet of machines that automaticallyaggregate purchasing in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a forward market and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aforward market and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a forward marketand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize requisitionand provisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aforward market and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a forward market and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aforward market and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a forward marketand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a forward marketand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofdetected conditions relating to an output parameter. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a forward market and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a forward marketand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofparameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing in a forwardmarket for energy. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a fleet of machines that automatically aggregate purchasingenergy credits in a forward market. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a machine that automatically purchases spectrum allocation in aforward market for network spectrum. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a machine that automatically sells its compute capacity on aforward market for compute capacity. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a machine that automatically sells its compute storage capacityon a forward market for storage capacity. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a machine that automatically sells its energy storage capacity ona forward market for energy storage capacity. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a machine that automatically sells its network bandwidth on aforward market for network capacity. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a fleet of machines that automatically purchase spectrumallocation in a forward market for network spectrum. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a forward market forenergy and having a fleet of machines that automatically optimize energyutilization for compute task allocation. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a forwardmarket for energy and having a fleet of machines that automaticallyaggregate data on collective optimization of forward market purchases ofenergy credits. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of network spectrum.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing in a forwardmarket for energy and having a fleet of machines that automaticallyaggregate data on collective optimization of forward market sales ofcompute capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a machine that automatically purchases its energy in a spotmarket for energy. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a machine that automatically purchases energy credits in a spotmarket. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically aggregate purchasing in aforward market for energy and having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a forwardmarket for energy and having a fleet of machines that automaticallyaggregate purchasing energy credits in a spot market. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a forward market forenergy and having a machine that automatically purchases spectrumallocation in a spot market for network spectrum. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a forward market forenergy and having a fleet of machines that automatically purchasespectrum allocation in a spot market for network spectrum. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a forwardmarket for energy and having a fleet of machines that automaticallyoptimize energy utilization for compute task allocation. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a forward market forenergy and having a fleet of machines that automatically aggregate dataon collective optimization of spot market purchases of energy. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a forwardmarket for energy and having a fleet of machines that automaticallyaggregate data on collective optimization of spot market purchases ofenergy credits. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of network spectrum. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a forwardmarket for energy and having a fleet of machines that automatically selltheir aggregate compute capacity on a forward market for computecapacity. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a forward market for energy and having a fleet of machinesthat automatically sell their aggregate compute storage capacity on aforward market for storage capacity. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a fleet of machines that automatically sell their aggregateenergy storage capacity on a forward market for energy storage capacity.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing in a forwardmarket for energy and having a fleet of machines that automatically selltheir aggregate network bandwidth on a forward market for networkcapacity. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a forward market for energy and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a forward market forenergy and having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from socialmedia data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from social media datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a forward market for energy and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a forward market forenergy and having a machine that automatically executes an arbitragestrategy for purchase or sale of compute capacity by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a forward market for energy and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of network spectrum or bandwidth by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a forward market for energy and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of energy credits by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a forwardmarket for energy and having a machine that automatically allocates itsenergy capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a forward market forenergy and having a machine that automatically allocates its computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a machine that automatically allocates its networking capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a fleet of machines that automatically allocate collective energycapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a fleet of machines that automatically allocate collectivecompute capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a forward market forenergy and having a fleet of machines that automatically allocatecollective networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a forwardmarket for energy and having a smart contract wrapper using adistributed ledger wherein the smart contract embeds IP licensing termsfor intellectual property embedded in the distributed ledger and whereinexecuting an operation on the distributed ledger provides access to theintellectual property and commits the executing party to the IPlicensing terms. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toan aggregate stack of intellectual property. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toagree to an apportionment of royalties among the parties in the ledger.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing in a forwardmarket for energy and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a forwardmarket for energy and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to commit aparty to a contract term. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a distributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a distributed ledger that tokenizes executable algorithmic logic,such that operation on the distributed ledger provides provable accessto the executable algorithmic logic. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a distributed ledger that tokenizes a 3D printer instruction set,such that operation on the distributed ledger provides provable accessto the instruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a distributed ledger that tokenizes an instruction set for acoating process, such that operation on the distributed ledger providesprovable access to the instruction set. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a distributed ledger that tokenizes an instruction set for asemiconductor fabrication process, such that operation on thedistributed ledger provides provable access to the fabrication process.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing in a forwardmarket for energy and having a distributed ledger that tokenizes afirmware program, such that operation on the distributed ledger providesprovable access to the firmware program. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a distributed ledger that tokenizes an instruction set for anFPGA, such that operation on the distributed ledger provides provableaccess to the FPGA. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a distributed ledger that tokenizes serverless code logic, suchthat operation on the distributed ledger provides provable access to theserverless code logic. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a distributed ledger that tokenizes an instruction set for acrystal fabrication system, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a forward market forenergy and having a distributed ledger that tokenizes an instruction setfor a food preparation process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a forward market forenergy and having a distributed ledger that tokenizes an instruction setfor a polymer production process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a forward market forenergy and having a distributed ledger that tokenizes an instruction setfor chemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a forward market forenergy and having a distributed ledger that tokenizes an instruction setfor a biological production process, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a forwardmarket for energy and having a distributed ledger that tokenizes a tradesecret with an expert wrapper, such that operation on the distributedledger provides provable access to the trade secret and the wrapperprovides validation of the trade secret by the expert. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a forward market forenergy and having a distributed ledger that aggregates views of a tradesecret into a chain that proves which and how many parties have viewedthe trade secret. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a distributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a forward market forenergy and having a distributed ledger that tokenizes an item ofintellectual property and a reporting system that reports an analyticresult based on the operations performed on the distributed ledger orthe intellectual property. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a distributed ledger that aggregates a set of instructions, wherean operation on the distributed ledger adds at least one instruction toa pre-existing set of instructions to provide a modified set ofinstructions. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a forward market for energy and having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a forwardmarket for energy and having a smart wrapper for a cryptocurrency cointhat directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a self-executing cryptocurrency coin that commits a transactionupon recognizing a location-based parameter that provides favorable taxtreatment. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a forward market for energy and having an expert systemthat uses machine learning to optimize the execution of cryptocurrencytransactions based on tax status. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving an expert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a forward market forenergy and having an expert system that uses machine learning tooptimize the execution of a cryptocurrency transaction based on realtime energy price information for an available energy source. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a forwardmarket for energy and having an expert system that uses machine learningto optimize the execution of a cryptocurrency transaction based on anunderstanding of available energy sources to power computing resourcesto execute the transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving an expert system that uses machine learning to optimize chargingand recharging cycle of a rechargeable battery system to provide energyfor execution of a cryptocurrency transaction. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a forward market for energy and having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a forwardmarket for energy and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a forward market forenergy and having an expert system that predicts a forward market pricein a market based on an understanding obtained by analyzing socialnetwork data sources and executes a cryptocurrency transaction based onthe forward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving an expert system that predicts a forward market price in anenergy market based on an understanding obtained by analyzing Internetof Things data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving an expert system that predicts a forward market price in anenergy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving an expert system that predicts a forward market price in a marketfor computing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving an expert system that predicts a forward market price in a marketfor spectrum or network bandwidth based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving an expert system that predicts a forward market price in a marketfor computing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving an expert system that predicts a forward market price in a marketfor advertising based on an understanding obtained by analyzing Internetof Things data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving an expert system that predicts a forward market price in a marketfor advertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a forward market for energy and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a forwardmarket for energy and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom human behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a forward market for energy and having an expert systemthat predicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing social datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a forward market for energy and having an intelligentagent that is configured to solicit the attention resources of anotherexternal intelligent agent. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a machine that automatically purchases attention resources in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a fleet of machines that automatically aggregate purchasing in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a likelihoodof a facility production outcome. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a facilityproduction outcome. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing in a forwardmarket for energy and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize requisitionand provisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a forward market for energy and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a forwardmarket for energy and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a forwardmarket for energy and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a forwardmarket for energy and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a forward market forenergy and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a forwardmarket for energy and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a forwardmarket for energy and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to an output parameter. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a forwardmarket for energy and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a forward market for energy andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of parametersreceived from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing energycredits in a forward market. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a machine that automatically purchases spectrum allocation ina forward market for network spectrum. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a machine that automatically sells its compute capacity on aforward market for compute capacity. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a machine that automatically sells its compute storagecapacity on a forward market for storage capacity. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aforward market and having a machine that automatically sells its energystorage capacity on a forward market for energy storage capacity. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a forward market and having a machine that automatically sells itsnetwork bandwidth on a forward market for network capacity. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a forward market and having a fleet of machines that automaticallypurchase spectrum allocation in a forward market for network spectrum.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing energycredits in a forward market and having a fleet of machines thatautomatically optimize energy utilization for compute task allocation.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing energycredits in a forward market and having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy credits.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing energycredits in a forward market and having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a fleet of machines that automatically aggregate data oncollective optimization of forward market sales of compute capacity. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a forward market and having a machine that automatically purchasesits energy in a spot market for energy. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a machine that automatically purchases energy credits in aspot market. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a forward market and having a fleet ofmachines that automatically aggregate purchasing in a spot market forenergy. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically aggregate purchasingenergy credits in a forward market and having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a forward market and having a machine that automatically purchasesspectrum allocation in a spot market for network spectrum. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a forward market and having a fleet of machines that automaticallypurchase spectrum allocation in a spot market for network spectrum. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a forward market and having a fleet of machines that automaticallyoptimize energy utilization for compute task allocation. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aforward market and having a fleet of machines that automaticallyaggregate data on collective optimization of spot market purchases ofenergy. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically aggregate purchasingenergy credits in a forward market and having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of network spectrum. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a forward market and having a fleet of machines that automaticallysell their aggregate compute capacity on a forward market for computecapacity. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a forward market and having a fleet ofmachines that automatically sell their aggregate compute storagecapacity on a forward market for storage capacity. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aforward market and having a fleet of machines that automatically selltheir aggregate energy storage capacity on a forward market for energystorage capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a fleet of machines that automatically sell their aggregatenetwork bandwidth on a forward market for network capacity. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a forward market and having a machine that automatically forecastsforward market pricing of energy prices based on information collectedfrom social media data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a machine that automatically forecasts forward market pricingof network spectrum based on information collected from social mediadata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a forward market and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aforward market and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromsocial media data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a machine that automatically executes an arbitrage strategyfor purchase or sale of compute capacity by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a forward market and having a machine that automatically executes anarbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a forward market and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a forward market and having a machine that automatically executes anarbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a forward market and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy credits by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a machine that automatically allocates its energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a machine that automatically allocates its compute capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a machine that automatically allocates its networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a fleet of machines that automatically allocate collectiveenergy capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aforward market and having a fleet of machines that automaticallyallocate collective compute capacity among a core task, a compute task,an energy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a forward market and having a fleet of machines that automaticallyallocate collective networking capacity among a core task, a computetask, an energy storage task, a data storage task and a networking task.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing energycredits in a forward market and having a smart contract wrapper using adistributed ledger wherein the smart contract embeds IP licensing termsfor intellectual property embedded in the distributed ledger and whereinexecuting an operation on the distributed ledger provides access to theintellectual property and commits the executing party to the IPlicensing terms. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toan aggregate stack of intellectual property. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toagree to an apportionment of royalties among the parties in the ledger.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing energycredits in a forward market and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to commit a party to a contractterm. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically aggregate purchasingenergy credits in a forward market and having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aforward market and having a distributed ledger that tokenizes executablealgorithmic logic, such that operation on the distributed ledgerprovides provable access to the executable algorithmic logic. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a forward market and having a distributed ledger that tokenizes a 3Dprinter instruction set, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aforward market and having a distributed ledger that tokenizes aninstruction set for a coating process, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a forward market and having a distributed ledger that tokenizes aninstruction set for a semiconductor fabrication process, such thatoperation on the distributed ledger provides provable access to thefabrication process. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a distributed ledger that tokenizes a firmware program, suchthat operation on the distributed ledger provides provable access to thefirmware program. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a distributed ledger that tokenizes an instruction set for anFPGA, such that operation on the distributed ledger provides provableaccess to the FPGA. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a distributed ledger that tokenizes serverless code logic,such that operation on the distributed ledger provides provable accessto the serverless code logic. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a distributed ledger that tokenizes an instruction set for acrystal fabrication system, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aforward market and having a distributed ledger that tokenizes aninstruction set for a food preparation process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing energycredits in a forward market and having a distributed ledger thattokenizes an instruction set for a polymer production process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a distributed ledger that tokenizes an instruction set forchemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aforward market and having a distributed ledger that tokenizes aninstruction set for a biological production process, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically aggregate purchasingenergy credits in a forward market and having a distributed ledger thattokenizes a trade secret with an expert wrapper, such that operation onthe distributed ledger provides provable access to the trade secret andthe wrapper provides validation of the trade secret by the expert. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a forward market and having a distributed ledger that aggregatesviews of a trade secret into a chain that proves which and how manyparties have viewed the trade secret. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a distributed ledger that tokenizes an instruction set, suchthat operation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aforward market and having a distributed ledger that tokenizes an item ofintellectual property and a reporting system that reports an analyticresult based on the operations performed on the distributed ledger orthe intellectual property. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a distributed ledger that aggregates a set of instructions,where an operation on the distributed ledger adds at least oneinstruction to a pre-existing set of instructions to provide a modifiedset of instructions. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a smart wrapper for management of a distributed ledger thataggregates sets of instructions, where the smart wrapper managesallocation of instruction sub-sets to the distributed ledger and accessto the instruction sub-sets. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a self-executing cryptocurrency coin that commits atransaction upon recognizing a location-based parameter that providesfavorable tax treatment. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having an expert system that uses machine learning to optimize theexecution of cryptocurrency transactions based on tax status. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a forward market and having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having an expert system that uses machine learning to optimize theexecution of a cryptocurrency transaction based on real time energyprice information for an available energy source. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aforward market and having an expert system that uses machine learning tooptimize the execution of a cryptocurrency transaction based on anunderstanding of available energy sources to power computing resourcesto execute the transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a forward market and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a forward market and having an expertsystem that predicts a forward market price in a market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a forward market and having an expertsystem that predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a forward market and having an expertsystem that predicts a forward market price in an energy market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a forward market and having an expert system that predicts a forwardmarket price in an energy market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having an expert system that predicts a forward market price in amarket for computing resources based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aforward market and having an expert system that predicts a forwardmarket price in a market for computing resources based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a forward market and having an expert system that predicts a forwardmarket price in a market for advertising based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aforward market and having an expert system that predicts a forwardmarket price in a market for advertising based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aforward market and having a machine that automatically forecasts forwardmarket pricing of energy prices based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a machine that automatically forecasts forward market pricingof network spectrum based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a machine that automatically forecasts forward market pricingof energy credits based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a machine that automatically forecasts forward market valueof compute capability based on information collected from automatedagent behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a machine that automatically forecasts forward market pricingof energy prices based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a machine that automatically forecasts forward market pricingof network spectrum based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a machine that automatically forecasts forward market pricingof energy credits based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a machine that automatically forecasts forward market valueof compute capability based on information collected from businessentity behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a machine that automatically forecasts forward market pricingof energy prices based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a forward market and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a forward market and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom human behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a machine that automatically forecasts forward market valueof compute capability based on information collected from humanbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing social data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having an intelligent agent that is configured to solicit theattention resources of another external intelligent agent. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a forward market and having a machine that automatically purchasesattention resources in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aforward market and having a fleet of machines that automaticallyaggregate purchasing in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aforward market and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a likelihood of a facility production outcome. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aforward market and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a facility production outcome. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing energycredits in a forward market and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource outputselection among a set of available outputs. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize requisitionand provisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a forward market and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a forward market and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a forward market and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a forward market and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to at least one of aninput resource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aforward market and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a forward market and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a forward market and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofdetected conditions relating to a utilization parameter for the outputof the facility. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a forward marketand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofparameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases spectrum allocation in a forwardmarket for network spectrum. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a machine that automatically sells its compute capacity on aforward market for compute capacity. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a machine that automatically sells its compute storagecapacity on a forward market for storage capacity. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a forward market fornetwork spectrum and having a machine that automatically sells itsenergy storage capacity on a forward market for energy storage capacity.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having a machine that automaticallysells its network bandwidth on a forward market for network capacity. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a fleet of machines that automatically optimize energyutilization for compute task allocation. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of network spectrum.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a machine that automatically purchases its energy in a spotmarket for energy. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a machine that automatically purchases energy credits in aspot market. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases spectrum allocationin a forward market for network spectrum and having a fleet of machinesthat automatically aggregate purchasing in a spot market for energy. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases spectrum allocationin a forward market for network spectrum and having a fleet of machinesthat automatically optimize energy utilization for compute taskallocation. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases spectrum allocationin a forward market for network spectrum and having a fleet of machinesthat automatically aggregate data on collective optimization of spotmarket purchases of energy. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy credits. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a fleet of machines that automatically sell their aggregatecompute capacity on a forward market for compute capacity. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a fleet of machines that automatically sell their aggregateenergy storage capacity on a forward market for energy storage capacity.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a machine that automatically forecasts forward market pricingof energy prices based on information collected from social media datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases spectrum allocationin a forward market for network spectrum and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a forward market fornetwork spectrum and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom social media data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a machine that automatically forecasts forward market valueof compute capability based on information collected from social mediadata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases spectrum allocationin a forward market for network spectrum and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a machine that automatically executes an arbitrage strategyfor purchase or sale of energy storage capacity by testing a spot marketfor compute capacity with a small transaction and rapidly executing alarger transaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of network spectrumor bandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a machine that automatically executes an arbitrage strategyfor purchase or sale of energy by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy credits bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases spectrum allocationin a forward market for network spectrum and having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking task.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a fleet of machines that automatically allocate collectivecompute capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a forward market fornetwork spectrum and having a fleet of machines that automaticallyallocate collective networking capacity among a core task, a computetask, an energy storage task, a data storage task and a networking task.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having a smart contract wrapper using adistributed ledger wherein the smart contract embeds IP licensing termsfor intellectual property embedded in the distributed ledger and whereinexecuting an operation on the distributed ledger provides access to theintellectual property and commits the executing party to the IPlicensing terms. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toan aggregate stack of intellectual property. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a forward market fornetwork spectrum and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to agree to an apportionment of royalties amongthe parties in the ledger. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toan aggregate stack of intellectual property. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a forward market fornetwork spectrum and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to commit aparty to a contract term. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a distributed ledger that tokenizes an instruction set, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a distributed ledger that tokenizes executable algorithmiclogic, such that operation on the distributed ledger provides provableaccess to the executable algorithmic logic. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a forward market fornetwork spectrum and having a distributed ledger that tokenizes a 3Dprinter instruction set, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a forward market fornetwork spectrum and having a distributed ledger that tokenizes aninstruction set for a coating process, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having a distributed ledger thattokenizes an instruction set for a semiconductor fabrication process,such that operation on the distributed ledger provides provable accessto the fabrication process. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a distributed ledger that tokenizes a firmware program, suchthat operation on the distributed ledger provides provable access to thefirmware program. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a distributed ledger that tokenizes an instruction set for anFPGA, such that operation on the distributed ledger provides provableaccess to the FPGA. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a distributed ledger that tokenizes serverless code logic,such that operation on the distributed ledger provides provable accessto the serverless code logic. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a distributed ledger that tokenizes an instruction set for acrystal fabrication system, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a forward market fornetwork spectrum and having a distributed ledger that tokenizes aninstruction set for a food preparation process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having a distributed ledger thattokenizes an instruction set for a polymer production process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a distributed ledger that tokenizes an instruction set forchemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a forward market fornetwork spectrum and having a distributed ledger that tokenizes aninstruction set for a biological production process, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically purchases spectrum allocation in aforward market for network spectrum and having a distributed ledger thattokenizes a trade secret with an expert wrapper, such that operation onthe distributed ledger provides provable access to the trade secret andthe wrapper provides validation of the trade secret by the expert. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a forward market fornetwork spectrum and having a distributed ledger that tokenizes aninstruction set, such that operation on the distributed ledger providesprovable access to the instruction set and execution of the instructionset on a system results in recording a transaction in the distributedledger. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically purchases spectrum allocation in aforward market for network spectrum and having a distributed ledger thattokenizes an item of intellectual property and a reporting system thatreports an analytic result based on the operations performed on thedistributed ledger or the intellectual property. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a forward market fornetwork spectrum and having a distributed ledger that aggregates a setof instructions, where an operation on the distributed ledger adds atleast one instruction to a pre-existing set of instructions to provide amodified set of instructions. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a smart wrapper for management of a distributed ledger thataggregates sets of instructions, where the smart wrapper managesallocation of instruction sub-sets to the distributed ledger and accessto the instruction sub-sets. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a self-executing cryptocurrency coin that commits atransaction upon recognizing a location-based parameter that providesfavorable tax treatment. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having an expert system that uses machine learning to optimize theexecution of cryptocurrency transactions based on tax status. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having an expert system that uses machine learning to optimize theexecution of a cryptocurrency transaction based on real time energyprice information for an available energy source. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a forward market fornetwork spectrum and having an expert system that uses machine learningto optimize the execution of a cryptocurrency transaction based on anunderstanding of available energy sources to power computing resourcesto execute the transaction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases spectrum allocationin a forward market for network spectrum and having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases spectrum allocationin a forward market for network spectrum and having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases spectrum allocationin a forward market for network spectrum and having an expert systemthat predicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a forward market fornetwork spectrum and having an expert system that predicts a forwardmarket price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases spectrum allocationin a forward market for network spectrum and having an expert systemthat predicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases spectrum allocationin a forward market for network spectrum and having an expert systemthat predicts a forward market price in a market for advertising basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases spectrum allocationin a forward market for network spectrum and having an expert systemthat predicts a forward market price in a market for advertising basedon an understanding obtained by analyzing social network data sourcesand executes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a forward market fornetwork spectrum and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a forward market fornetwork spectrum and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom automated agent behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a forward market fornetwork spectrum and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a forward market fornetwork spectrum and having a machine that automatically forecastsforward market pricing of energy prices based on information collectedfrom business entity behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a forward market fornetwork spectrum and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a forward market fornetwork spectrum and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom business entity behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a forward market fornetwork spectrum and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a forward market fornetwork spectrum and having a machine that automatically forecastsforward market pricing of energy prices based on information collectedfrom human behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a machine that automatically forecasts forward market pricingof network spectrum based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases spectrum allocationin a forward market for network spectrum and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing social data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having an intelligent agent that isconfigured to solicit the attention resources of another externalintelligent agent. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a machine that automatically purchases attention resources ina forward market for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a fleet of machines that automatically aggregate purchasingin a forward market for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a likelihoodof a facility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a facilityproduction outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource outputselection among a set of available outputs. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a forward market fornetwork spectrum and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimizeconfiguration of available energy and compute resources to produce afavorable facility resource configuration profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize selectionand configuration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a forward market fornetwork spectrum and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a forward market fornetwork spectrum and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a forwardmarket for network spectrum and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases spectrum allocationin a forward market for network spectrum and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofdetected conditions relating to a utilization parameter for the outputof the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a forward market for network spectrumand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofparameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically sells its compute capacity on a forwardmarket for compute capacity. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga machine that automatically sells its compute storage capacity on aforward market for storage capacity. In embodiments, provided herein isa transaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga machine that automatically sells its energy storage capacity on aforward market for energy storage capacity. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having a machine that automatically sells its networkbandwidth on a forward market for network capacity. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having a fleet of machines that automatically purchasespectrum allocation in a forward market for network spectrum. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute capacity on a forwardmarket for compute capacity and having a fleet of machines thatautomatically optimize energy utilization for compute task allocation.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically sells its compute capacity on a forwardmarket for compute capacity and having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute capacity on a forwardmarket for compute capacity and having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum. In embodiments, provided herein isa transaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market sales of compute capacity. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute capacity on a forwardmarket for compute capacity and having a machine that automaticallypurchases its energy in a spot market for energy. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having a machine that automatically purchases energycredits in a spot market. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga fleet of machines that automatically aggregate purchasing in a spotmarket for energy. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga fleet of machines that automatically aggregate purchasing energycredits in a spot market. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga machine that automatically purchases spectrum allocation in a spotmarket for network spectrum. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga fleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga fleet of machines that automatically optimize energy utilization forcompute task allocation. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga fleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having a fleet of machines that automatically aggregatedata on collective optimization of spot market purchases of energycredits. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute capacity ona forward market for compute capacity and having a fleet of machinesthat automatically aggregate data on collective optimization of spotmarket purchases of network spectrum. In embodiments, provided herein isa transaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga fleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having a fleet of machines that automatically sell theiraggregate compute storage capacity on a forward market for storagecapacity. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute capacity ona forward market for compute capacity and having a fleet of machinesthat automatically sell their aggregate energy storage capacity on aforward market for energy storage capacity. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having a fleet of machines that automatically sell theiraggregate network bandwidth on a forward market for network capacity. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute capacity on a forwardmarket for compute capacity and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected fromsocial media data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga machine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sources.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically sells its compute capacity on a forwardmarket for compute capacity and having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having a machine that automatically executes an arbitragestrategy for purchase or sale of compute capacity by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute capacity ona forward market for compute capacity and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having a machine that automatically executes an arbitragestrategy for purchase or sale of network spectrum or bandwidth bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute capacity ona forward market for compute capacity and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy credits by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having a machine that automatically allocates its energycapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallysells its compute capacity on a forward market for compute capacity andhaving a machine that automatically allocates its compute capacity amonga core task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga machine that automatically allocates its networking capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga fleet of machines that automatically allocate collective energycapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallysells its compute capacity on a forward market for compute capacity andhaving a fleet of machines that automatically allocate collectivecompute capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having a fleet of machines that automatically allocatecollective networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute capacity on a forwardmarket for compute capacity and having a smart contract wrapper using adistributed ledger wherein the smart contract embeds IP licensing termsfor intellectual property embedded in the distributed ledger and whereinexecuting an operation on the distributed ledger provides access to theintellectual property and commits the executing party to the IPlicensing terms. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga distributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to an aggregatestack of intellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga distributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to agree to anapportionment of royalties among the parties in the ledger. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute capacity on a forwardmarket for compute capacity and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga distributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to commit a party to a contract term. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute capacity on a forwardmarket for compute capacity and having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having a distributed ledger that tokenizes executablealgorithmic logic, such that operation on the distributed ledgerprovides provable access to the executable algorithmic logic. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute capacity on a forwardmarket for compute capacity and having a distributed ledger thattokenizes a 3D printer instruction set, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute capacity on a forwardmarket for compute capacity and having a distributed ledger thattokenizes an instruction set for a coating process, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically sells its compute capacity on aforward market for compute capacity and having a distributed ledger thattokenizes an instruction set for a semiconductor fabrication process,such that operation on the distributed ledger provides provable accessto the fabrication process. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga distributed ledger that tokenizes a firmware program, such thatoperation on the distributed ledger provides provable access to thefirmware program. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga distributed ledger that tokenizes an instruction set for an FPGA, suchthat operation on the distributed ledger provides provable access to theFPGA. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically sells its compute capacity on aforward market for compute capacity and having a distributed ledger thattokenizes serverless code logic, such that operation on the distributedledger provides provable access to the serverless code logic. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute capacity on a forwardmarket for compute capacity and having a distributed ledger thattokenizes an instruction set for a crystal fabrication system, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga distributed ledger that tokenizes an instruction set for a foodpreparation process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having a distributed ledger that tokenizes an instructionset for a polymer production process, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute capacity on a forwardmarket for compute capacity and having a distributed ledger thattokenizes an instruction set for chemical synthesis process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga distributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having a distributed ledger that tokenizes a trade secretwith an expert wrapper, such that operation on the distributed ledgerprovides provable access to the trade secret and the wrapper providesvalidation of the trade secret by the expert. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having a distributed ledger that aggregates views of atrade secret into a chain that proves which and how many parties haveviewed the trade secret. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga distributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having a distributed ledger that tokenizes an item ofintellectual property and a reporting system that reports an analyticresult based on the operations performed on the distributed ledger orthe intellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga distributed ledger that aggregates a set of instructions, where anoperation on the distributed ledger adds at least one instruction to apre-existing set of instructions to provide a modified set ofinstructions. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute capacity ona forward market for compute capacity and having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute capacity on a forwardmarket for compute capacity and having a smart wrapper for acryptocurrency coin that directs execution of a transaction involvingthe coin to a geographic location based on tax treatment of at least oneof the coin and the transaction in the geographic location. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute capacity on a forwardmarket for compute capacity and having a self-executing cryptocurrencycoin that commits a transaction upon recognizing a location-basedparameter that provides favorable tax treatment. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having an expert system that uses machine learning tooptimize the execution of cryptocurrency transactions based on taxstatus. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically sells its compute capacity on aforward market for compute capacity and having an expert system thataggregates regulatory information covering cryptocurrency transactionsand automatically selects a jurisdiction for an operation based on theregulatory information. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havingan expert system that uses machine learning to optimize the execution ofa cryptocurrency transaction based on real time energy price informationfor an available energy source. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havingan expert system that uses machine learning to optimize the execution ofa cryptocurrency transaction based on an understanding of availableenergy sources to power computing resources to execute the transaction.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically sells its compute capacity on a forwardmarket for compute capacity and having an expert system that usesmachine learning to optimize charging and recharging cycle of arechargeable battery system to provide energy for execution of acryptocurrency transaction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havingan expert system that predicts a forward market price in a market basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute capacity ona forward market for compute capacity and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having an expert system that predicts a forward marketprice in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having an expert system that predicts a forward marketprice in a market based on an understanding obtained by analyzing socialnetwork data sources and executes a cryptocurrency transaction based onthe forward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havingan expert system that predicts a forward market price in an energymarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havingan expert system that predicts a forward market price in an energymarket based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute capacity ona forward market for compute capacity and having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute capacity ona forward market for compute capacity and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havingan expert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havingan expert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havingan expert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute capacity ona forward market for compute capacity and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically sells its compute capacity on a forwardmarket for compute capacity and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallysells its compute capacity on a forward market for compute capacity andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga machine that automatically forecasts forward market pricing of networkspectrum based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute capacity ona forward market for compute capacity and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically sells its compute capacity on a forwardmarket for compute capacity and having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute capacity on a forwardmarket for compute capacity and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga machine that automatically forecasts forward market pricing of energycredits based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute capacity ona forward market for compute capacity and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute capacity on a forwardmarket for compute capacity and having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing social data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute capacity on a forwardmarket for compute capacity and having an intelligent agent that isconfigured to solicit the attention resources of another externalintelligent agent. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga machine that automatically purchases attention resources in a forwardmarket for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga fleet of machines that automatically aggregate purchasing in a forwardmarket for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute capacity ona forward market for compute capacity and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource utilizationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizeprovisioning and allocation of energy and compute resources to produce afavorable facility resource output selection among a set of availableoutputs. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute capacity ona forward market for compute capacity and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize requisition and provisioning of availableenergy and compute resources to produce a favorable facility inputresource profile among a set of available profiles. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizeconfiguration of available energy and compute resources to produce afavorable facility resource configuration profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallysells its compute capacity on a forward market for compute capacity andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to at least one of an input resource, afacility resource, an output parameter and an external condition relatedto the output of the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute capacity on a forward market for compute capacity and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of input resources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute capacity on a forwardmarket for compute capacity and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to an output parameter.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically sells its compute capacity on a forwardmarket for compute capacity and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a utilizationparameter for the output of the facility. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its compute capacity on a forward market for computecapacity and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofparameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically sells its compute storage capacity on aforward market for storage capacity. In embodiments, provided herein isa transaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having a machine that automatically sells its energy storagecapacity on a forward market for energy storage capacity. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute storage capacity on aforward market for storage capacity and having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having a fleet of machines that automatically purchase spectrumallocation in a forward market for network spectrum. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute storage capacity on a forward market forstorage capacity and having a fleet of machines that automaticallyoptimize energy utilization for compute task allocation. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute storage capacity on a forward market forstorage capacity and having a fleet of machines that automaticallyaggregate data on collective optimization of forward market purchases ofenergy. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically sells its compute storage capacityon a forward market for storage capacity and having a fleet of machinesthat automatically aggregate data on collective optimization of forwardmarket purchases of energy credits. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of network spectrum.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically sells its compute storage capacity on aforward market for storage capacity and having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having a machine that automatically purchases its energy in a spotmarket for energy. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having a machine that automatically purchases energy credits in aspot market. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute storagecapacity on a forward market for storage capacity and having a fleet ofmachines that automatically aggregate purchasing in a spot market forenergy. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically sells its compute storage capacityon a forward market for storage capacity and having a fleet of machinesthat automatically aggregate purchasing energy credits in a spot market.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically sells its compute storage capacity on aforward market for storage capacity and having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute storagecapacity on a forward market for storage capacity and having a fleet ofmachines that automatically purchase spectrum allocation in a spotmarket for network spectrum. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having a fleet of machines that automatically optimize energyutilization for compute task allocation. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallysells its compute storage capacity on a forward market for storagecapacity and having a fleet of machines that automatically aggregatedata on collective optimization of spot market purchases of energy. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute storage capacity on aforward market for storage capacity and having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of network spectrum. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute storage capacity on aforward market for storage capacity and having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having a fleet of machines that automatically sell their aggregatecompute storage capacity on a forward market for storage capacity. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute storage capacity on aforward market for storage capacity and having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having a fleet of machines that automatically sell their aggregatenetwork bandwidth on a forward market for network capacity. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute storage capacity on aforward market for storage capacity and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute storage capacity on a forward market forstorage capacity and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from social media data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its compute storage capacity on a forward market forstorage capacity and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom social media data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having a machine that automatically forecasts forward market valueof compute capability based on information collected from social mediadata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute storagecapacity on a forward market for storage capacity and having a machinethat automatically executes an arbitrage strategy for purchase or saleof compute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having a machine that automatically executes an arbitrage strategyfor purchase or sale of energy storage capacity by testing a spot marketfor compute capacity with a small transaction and rapidly executing alarger transaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute storage capacity on aforward market for storage capacity and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute storage capacity on aforward market for storage capacity and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having a machine that automatically executes an arbitrage strategyfor purchase or sale of energy credits by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute storage capacity on aforward market for storage capacity and having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking task.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically sells its compute storage capacity on aforward market for storage capacity and having a machine thatautomatically allocates its compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having a machine that automatically allocates its networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallysells its compute storage capacity on a forward market for storagecapacity and having a fleet of machines that automatically allocatecollective energy capacity among a core task, a compute task, an energystorage task, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute storage capacity on a forward market forstorage capacity and having a fleet of machines that automaticallyallocate collective compute capacity among a core task, a compute task,an energy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute storage capacity on aforward market for storage capacity and having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having a smart contract wrapper using a distributed ledger whereinthe smart contract embeds IP licensing terms for intellectual propertyembedded in the distributed ledger and wherein executing an operation onthe distributed ledger provides access to the intellectual property andcommits the executing party to the IP licensing terms. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute storage capacity on a forward market forstorage capacity and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute storage capacity on aforward market for storage capacity and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to agree to an apportionment ofroyalties among the parties in the ledger. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its compute storage capacity on a forward market forstorage capacity and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute storage capacity on aforward market for storage capacity and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to commit a party to a contract term. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its compute storage capacity on a forward market forstorage capacity and having a distributed ledger that tokenizes aninstruction set, such that operation on the distributed ledger providesprovable access to the instruction set. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallysells its compute storage capacity on a forward market for storagecapacity and having a distributed ledger that tokenizes executablealgorithmic logic, such that operation on the distributed ledgerprovides provable access to the executable algorithmic logic. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute storage capacity on aforward market for storage capacity and having a distributed ledger thattokenizes a 3D printer instruction set, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute storage capacity on aforward market for storage capacity and having a distributed ledger thattokenizes an instruction set for a coating process, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically sells its compute storage capacityon a forward market for storage capacity and having a distributed ledgerthat tokenizes an instruction set for a semiconductor fabricationprocess, such that operation on the distributed ledger provides provableaccess to the fabrication process. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having a distributed ledger that tokenizes a firmware program, suchthat operation on the distributed ledger provides provable access to thefirmware program. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having a distributed ledger that tokenizes an instruction set for anFPGA, such that operation on the distributed ledger provides provableaccess to the FPGA. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having a distributed ledger that tokenizes serverless code logic,such that operation on the distributed ledger provides provable accessto the serverless code logic. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having a distributed ledger that tokenizes an instruction set for acrystal fabrication system, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute storage capacity on a forward market forstorage capacity and having a distributed ledger that tokenizes aninstruction set for a food preparation process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically sells its compute storage capacity on aforward market for storage capacity and having a distributed ledger thattokenizes an instruction set for a polymer production process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having a distributed ledger that tokenizes an instruction set forchemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute storage capacity on a forward market forstorage capacity and having a distributed ledger that tokenizes aninstruction set for a biological production process, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically sells its compute storage capacityon a forward market for storage capacity and having a distributed ledgerthat tokenizes a trade secret with an expert wrapper, such thatoperation on the distributed ledger provides provable access to thetrade secret and the wrapper provides validation of the trade secret bythe expert. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute storagecapacity on a forward market for storage capacity and having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute storage capacity on aforward market for storage capacity and having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set and execution ofthe instruction set on a system results in recording a transaction inthe distributed ledger. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having a distributed ledger that tokenizes an item of intellectualproperty and a reporting system that reports an analytic result based onthe operations performed on the distributed ledger or the intellectualproperty. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute storagecapacity on a forward market for storage capacity and having adistributed ledger that aggregates a set of instructions, where anoperation on the distributed ledger adds at least one instruction to apre-existing set of instructions to provide a modified set ofinstructions. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute storagecapacity on a forward market for storage capacity and having a smartwrapper for management of a distributed ledger that aggregates sets ofinstructions, where the smart wrapper manages allocation of instructionsub-sets to the distributed ledger and access to the instructionsub-sets. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute storagecapacity on a forward market for storage capacity and having a smartwrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having a self-executing cryptocurrency coin that commits atransaction upon recognizing a location-based parameter that providesfavorable tax treatment. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having an expert system that uses machine learning to optimize theexecution of cryptocurrency transactions based on tax status. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute storage capacity on aforward market for storage capacity and having an expert system thataggregates regulatory information covering cryptocurrency transactionsand automatically selects a jurisdiction for an operation based on theregulatory information. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having an expert system that uses machine learning to optimize theexecution of a cryptocurrency transaction based on real time energyprice information for an available energy source. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute storage capacity on a forward market forstorage capacity and having an expert system that uses machine learningto optimize the execution of a cryptocurrency transaction based on anunderstanding of available energy sources to power computing resourcesto execute the transaction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute storage capacity on aforward market for storage capacity and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute storage capacity on a forward market forstorage capacity and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a cryptocurrency transaction based onthe forward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a cryptocurrency transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having an expert system that predicts a forward market price in anenergy market based on an understanding obtained by analyzing Internetof Things data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having an expert system that predicts a forward market price in anenergy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having an expert system that predicts a forward market price in amarket for computing resources based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallysells its compute storage capacity on a forward market for storagecapacity and having an expert system that predicts a forward marketprice in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute storage capacity on aforward market for storage capacity and having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute storagecapacity on a forward market for storage capacity and having an expertsystem that predicts a forward market price in a market for advertisingbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute storagecapacity on a forward market for storage capacity and having an expertsystem that predicts a forward market price in a market for advertisingbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute storagecapacity on a forward market for storage capacity and having a machinethat automatically forecasts forward market pricing of energy pricesbased on information collected from automated agent behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute storagecapacity on a forward market for storage capacity and having a machinethat automatically forecasts forward market pricing of network spectrumbased on information collected from automated agent behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute storagecapacity on a forward market for storage capacity and having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from automated agent behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute storagecapacity on a forward market for storage capacity and having a machinethat automatically forecasts forward market value of compute capabilitybased on information collected from automated agent behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute storagecapacity on a forward market for storage capacity and having a machinethat automatically forecasts forward market pricing of energy pricesbased on information collected from business entity behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute storagecapacity on a forward market for storage capacity and having a machinethat automatically forecasts forward market pricing of network spectrumbased on information collected from business entity behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute storagecapacity on a forward market for storage capacity and having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from business entity behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute storagecapacity on a forward market for storage capacity and having a machinethat automatically forecasts forward market value of compute capabilitybased on information collected from business entity behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute storagecapacity on a forward market for storage capacity and having a machinethat automatically forecasts forward market pricing of energy pricesbased on information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute storage capacity on aforward market for storage capacity and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute storage capacity on aforward market for storage capacity and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute storage capacity on aforward market for storage capacity and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute storage capacity on aforward market for storage capacity and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing social datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute storagecapacity on a forward market for storage capacity and having anintelligent agent that is configured to solicit the attention resourcesof another external intelligent agent. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallysells its compute storage capacity on a forward market for storagecapacity and having a machine that automatically purchases attentionresources in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its compute storage capacity on a forward market forstorage capacity and having a fleet of machines that automaticallyaggregate purchasing in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute storage capacity on a forward market forstorage capacity and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a likelihood of a facility production outcome. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute storage capacity on a forward market forstorage capacity and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a facility production outcome. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallysells its compute storage capacity on a forward market for storagecapacity and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizeprovisioning and allocation of energy and compute resources to produce afavorable facility resource utilization profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its compute storagecapacity on a forward market for storage capacity and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute storage capacity on aforward market for storage capacity and having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize requisition and provisioning of availableenergy and compute resources to produce a favorable facility inputresource profile among a set of available profiles. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute storage capacity on a forward market forstorage capacity and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize selectionand configuration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallysells its compute storage capacity on a forward market for storagecapacity and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute storage capacity on a forward market forstorage capacity and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute storage capacity on a forward market forstorage capacity and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its compute storage capacity on aforward market for storage capacity and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to a setof facility resources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofdetected conditions relating to an output parameter. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its compute storage capacity on a forward market forstorage capacity and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits compute storage capacity on a forward market for storage capacityand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofparameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically sells its energy storage capacity on aforward market for energy storage capacity. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its energy storage capacity on a forward market forenergy storage capacity and having a machine that automatically sellsits network bandwidth on a forward market for network capacity. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its energy storage capacity on aforward market for energy storage capacity and having a fleet ofmachines that automatically purchase spectrum allocation in a forwardmarket for network spectrum. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a fleet of machines that automatically optimizeenergy utilization for compute task allocation. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its energy storage capacity on a forward market forenergy storage capacity and having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a fleet of machines that automatically aggregatedata on collective optimization of forward market purchases of energycredits. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its energy storagecapacity on a forward market for energy storage capacity and having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its energy storage capacity on aforward market for energy storage capacity and having a fleet ofmachines that automatically aggregate data on collective optimization offorward market sales of compute capacity. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its energy storage capacity on a forward market forenergy storage capacity and having a machine that automaticallypurchases its energy in a spot market for energy. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its energy storage capacity on a forward market forenergy storage capacity and having a machine that automaticallypurchases energy credits in a spot market. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its energy storage capacity on a forward market forenergy storage capacity and having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its energy storage capacity on aforward market for energy storage capacity and having a fleet ofmachines that automatically aggregate purchasing energy credits in aspot market. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its energy storagecapacity on a forward market for energy storage capacity and having amachine that automatically purchases spectrum allocation in a spotmarket for network spectrum. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a fleet of machines that automatically purchasespectrum allocation in a spot market for network spectrum. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its energy storage capacity on aforward market for energy storage capacity and having a fleet ofmachines that automatically optimize energy utilization for compute taskallocation. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its energy storagecapacity on a forward market for energy storage capacity and having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its energy storage capacity on a forward market forenergy storage capacity and having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a fleet of machines that automatically aggregatedata on collective optimization of spot market purchases of networkspectrum. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its energy storagecapacity on a forward market for energy storage capacity and having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its energy storage capacity on a forward market forenergy storage capacity and having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a fleet of machines that automatically sell theiraggregate energy storage capacity on a forward market for energy storagecapacity. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its energy storagecapacity on a forward market for energy storage capacity and having afleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its energy storage capacity on a forward market forenergy storage capacity and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its energy storage capacity on a forward market forenergy storage capacity and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its energy storage capacity on a forward market forenergy storage capacity and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its energy storage capacity on a forward market forenergy storage capacity and having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its energy storage capacity on a forward market forenergy storage capacity and having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its energy storagecapacity on a forward market for energy storage capacity and having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its energy storage capacity on aforward market for energy storage capacity and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its energy storage capacity on aforward market for energy storage capacity and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a machine that automatically executes an arbitragestrategy for purchase or sale of energy credits by testing a spot marketfor compute capacity with a small transaction and rapidly executing alarger transaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its energy storage capacity on aforward market for energy storage capacity and having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking task.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically sells its energy storage capacity on aforward market for energy storage capacity and having a machine thatautomatically allocates its compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a machine that automatically allocates itsnetworking capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its energy storage capacity on a forward market forenergy storage capacity and having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a fleet of machines that automatically allocatecollective compute capacity among a core task, a compute task, an energystorage task, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its energy storage capacity on a forward market forenergy storage capacity and having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a smart contract wrapper using a distributed ledgerwherein the smart contract embeds IP licensing terms for intellectualproperty embedded in the distributed ledger and wherein executing anoperation on the distributed ledger provides access to the intellectualproperty and commits the executing party to the IP licensing terms. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its energy storage capacity on aforward market for energy storage capacity and having a distributedledger for aggregating intellectual property licensing terms, wherein asmart contract wrapper on the distributed ledger allows an operation onthe ledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a distributed ledger for aggregating intellectualproperty licensing terms, wherein a smart contract wrapper on thedistributed ledger allows an operation on the ledger to add intellectualproperty to agree to an apportionment of royalties among the parties inthe ledger. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its energy storagecapacity on a forward market for energy storage capacity and having adistributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to an aggregatestack of intellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a distributed ledger for aggregating intellectualproperty licensing terms, wherein a smart contract wrapper on thedistributed ledger allows an operation on the ledger to commit a partyto a contract term. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a distributed ledger that tokenizes an instructionset, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a distributed ledger that tokenizes executablealgorithmic logic, such that operation on the distributed ledgerprovides provable access to the executable algorithmic logic. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its energy storage capacity on aforward market for energy storage capacity and having a distributedledger that tokenizes a 3D printer instruction set, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically sells its energy storage capacity ona forward market for energy storage capacity and having a distributedledger that tokenizes an instruction set for a coating process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a distributed ledger that tokenizes an instructionset for a semiconductor fabrication process, such that operation on thedistributed ledger provides provable access to the fabrication process.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically sells its energy storage capacity on aforward market for energy storage capacity and having a distributedledger that tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its energy storage capacity on aforward market for energy storage capacity and having a distributedledger that tokenizes an instruction set for an FPGA, such thatoperation on the distributed ledger provides provable access to theFPGA. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically sells its energy storage capacity ona forward market for energy storage capacity and having a distributedledger that tokenizes serverless code logic, such that operation on thedistributed ledger provides provable access to the serverless codelogic. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically sells its energy storage capacity ona forward market for energy storage capacity and having a distributedledger that tokenizes an instruction set for a crystal fabricationsystem, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a distributed ledger that tokenizes an instructionset for a food preparation process, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its energy storage capacity on aforward market for energy storage capacity and having a distributedledger that tokenizes an instruction set for a polymer productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a distributed ledger that tokenizes an instructionset for chemical synthesis process, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its energy storage capacity on aforward market for energy storage capacity and having a distributedledger that tokenizes an instruction set for a biological productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a distributed ledger that tokenizes a trade secretwith an expert wrapper, such that operation on the distributed ledgerprovides provable access to the trade secret and the wrapper providesvalidation of the trade secret by the expert. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its energy storage capacity on a forward market forenergy storage capacity and having a distributed ledger that aggregatesviews of a trade secret into a chain that proves which and how manyparties have viewed the trade secret. In embodiments, provided herein isa transaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a distributed ledger that tokenizes an instructionset, such that operation on the distributed ledger provides provableaccess to the instruction set and execution of the instruction set on asystem results in recording a transaction in the distributed ledger. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its energy storage capacity on aforward market for energy storage capacity and having a distributedledger that tokenizes an item of intellectual property and a reportingsystem that reports an analytic result based on the operations performedon the distributed ledger or the intellectual property. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its energy storage capacity on a forward market forenergy storage capacity and having a distributed ledger that aggregatesa set of instructions, where an operation on the distributed ledger addsat least one instruction to a pre-existing set of instructions toprovide a modified set of instructions. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallysells its energy storage capacity on a forward market for energy storagecapacity and having a smart wrapper for management of a distributedledger that aggregates sets of instructions, where the smart wrappermanages allocation of instruction sub-sets to the distributed ledger andaccess to the instruction sub-sets. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a smart wrapper for a cryptocurrency coin thatdirects execution of a transaction involving the coin to a geographiclocation based on tax treatment of at least one of the coin and thetransaction in the geographic location. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallysells its energy storage capacity on a forward market for energy storagecapacity and having a self-executing cryptocurrency coin that commits atransaction upon recognizing a location-based parameter that providesfavorable tax treatment. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having an expert system that uses machine learning tooptimize the execution of cryptocurrency transactions based on taxstatus. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically sells its energy storage capacity ona forward market for energy storage capacity and having an expert systemthat aggregates regulatory information covering cryptocurrencytransactions and automatically selects a jurisdiction for an operationbased on the regulatory information. In embodiments, provided herein isa transaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having an expert system that uses machine learning tooptimize the execution of a cryptocurrency transaction based on realtime energy price information for an available energy source. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its energy storage capacity on aforward market for energy storage capacity and having an expert systemthat uses machine learning to optimize the execution of a cryptocurrencytransaction based on an understanding of available energy sources topower computing resources to execute the transaction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its energy storage capacity on a forward market forenergy storage capacity and having an expert system that uses machinelearning to optimize charging and recharging cycle of a rechargeablebattery system to provide energy for execution of a cryptocurrencytransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its energy storagecapacity on a forward market for energy storage capacity and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its energy storage capacity on aforward market for energy storage capacity and having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its energy storage capacity on aforward market for energy storage capacity and having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its energy storagecapacity on a forward market for energy storage capacity and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its energy storagecapacity on a forward market for energy storage capacity and having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its energy storagecapacity on a forward market for energy storage capacity and having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its energy storagecapacity on a forward market for energy storage capacity and having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having an expert system that predicts a forward marketprice in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its energy storage capacity on aforward market for energy storage capacity and having an expert systemthat predicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its energy storagecapacity on a forward market for energy storage capacity and having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having an expert system that predicts a forward marketprice in a market for advertising based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a machine that automatically forecasts forwardmarket pricing of energy prices based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallysells its energy storage capacity on a forward market for energy storagecapacity and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallysells its energy storage capacity on a forward market for energy storagecapacity and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallysells its energy storage capacity on a forward market for energy storagecapacity and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallysells its energy storage capacity on a forward market for energy storagecapacity and having a machine that automatically forecasts forwardmarket pricing of energy prices based on information collected frombusiness entity behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallysells its energy storage capacity on a forward market for energy storagecapacity and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected frombusiness entity behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallysells its energy storage capacity on a forward market for energy storagecapacity and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected frombusiness entity behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallysells its energy storage capacity on a forward market for energy storagecapacity and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected frombusiness entity behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallysells its energy storage capacity on a forward market for energy storagecapacity and having a machine that automatically forecasts forwardmarket pricing of energy prices based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having an expert system that predicts a forward marketprice in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing social data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its energy storage capacity on a forward market forenergy storage capacity and having an intelligent agent that isconfigured to solicit the attention resources of another externalintelligent agent. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a machine that automatically purchases attentionresources in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its energy storage capacity on a forward market forenergy storage capacity and having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its energy storage capacity on aforward market for energy storage capacity and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to predict afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits energy storage capacity on a forward market for energy storagecapacity and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizeprovisioning and allocation of energy and compute resources to produce afavorable facility resource utilization profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its energy storagecapacity on a forward market for energy storage capacity and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its energy storage capacity on aforward market for energy storage capacity and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize requisition andprovisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its energy storagecapacity on a forward market for energy storage capacity and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its energy storage capacity on aforward market for energy storage capacity and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallysells its energy storage capacity on a forward market for energy storagecapacity and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its energy storage capacity on a forward market forenergy storage capacity and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to at least one of aninput resource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its energy storage capacity on a forward market forenergy storage capacity and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of inputresources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its energy storagecapacity on a forward market for energy storage capacity and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of facility resources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallysells its energy storage capacity on a forward market for energy storagecapacity and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to an output parameter. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its energy storage capacity on a forward market forenergy storage capacity and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a utilizationparameter for the output of the facility. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its energy storage capacity on a forward market forenergy storage capacity and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of parameters received from a digital twin for thefacility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically sells its network bandwidth on a forwardmarket for network capacity. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a fleet of machines that automatically purchase spectrumallocation in a forward market for network spectrum. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having a fleet of machines that automaticallyoptimize energy utilization for compute task allocation. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having a fleet of machines that automaticallyaggregate data on collective optimization of forward market purchases ofenergy. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically sells its network bandwidth on aforward market for network capacity and having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of network spectrum.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically sells its network bandwidth on a forwardmarket for network capacity and having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a machine that automatically purchases its energy in a spotmarket for energy. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a machine that automatically purchases energy credits in a spotmarket. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically sells its network bandwidth on aforward market for network capacity and having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its network bandwidth on a forwardmarket for network capacity and having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its network bandwidth on a forwardmarket for network capacity and having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its network bandwidth on a forwardmarket for network capacity and having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its network bandwidthon a forward market for network capacity and having a fleet of machinesthat automatically optimize energy utilization for compute taskallocation. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its network bandwidthon a forward market for network capacity and having a fleet of machinesthat automatically aggregate data on collective optimization of spotmarket purchases of energy. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy credits. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its network bandwidth on a forwardmarket for network capacity and having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a fleet of machines that automatically sell their aggregatecompute capacity on a forward market for compute capacity. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its network bandwidth on a forwardmarket for network capacity and having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a fleet of machines that automatically sell their aggregateenergy storage capacity on a forward market for energy storage capacity.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically sells its network bandwidth on a forwardmarket for network capacity and having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from social media datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its network bandwidthon a forward market for network capacity and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom social media data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from social media datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its network bandwidthon a forward market for network capacity and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of energy storage capacity by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its network bandwidth on a forwardmarket for network capacity and having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of network spectrumor bandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of energy by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having a machine that automatically executes anarbitrage strategy for purchase or sale of energy credits by testing aspot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its network bandwidthon a forward market for network capacity and having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking task.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically sells its network bandwidth on a forwardmarket for network capacity and having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its network bandwidth on a forwardmarket for network capacity and having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its network bandwidth on a forwardmarket for network capacity and having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a fleet of machines that automatically allocate collectivecompute capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having a fleet of machines that automaticallyallocate collective networking capacity among a core task, a computetask, an energy storage task, a data storage task and a networking task.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically sells its network bandwidth on a forwardmarket for network capacity and having a smart contract wrapper using adistributed ledger wherein the smart contract embeds IP licensing termsfor intellectual property embedded in the distributed ledger and whereinexecuting an operation on the distributed ledger provides access to theintellectual property and commits the executing party to the IPlicensing terms. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toan aggregate stack of intellectual property. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to agree to an apportionment of royalties amongthe parties in the ledger. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toan aggregate stack of intellectual property. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to commit aparty to a contract term. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a distributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a distributed ledger that tokenizes executable algorithmic logic,such that operation on the distributed ledger provides provable accessto the executable algorithmic logic. In embodiments, provided herein isa transaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a distributed ledger that tokenizes a 3D printer instruction set,such that operation on the distributed ledger provides provable accessto the instruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a distributed ledger that tokenizes an instruction set for acoating process, such that operation on the distributed ledger providesprovable access to the instruction set. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallysells its network bandwidth on a forward market for network capacity andhaving a distributed ledger that tokenizes an instruction set for asemiconductor fabrication process, such that operation on thedistributed ledger provides provable access to the fabrication process.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically sells its network bandwidth on a forwardmarket for network capacity and having a distributed ledger thattokenizes a firmware program, such that operation on the distributedledger provides provable access to the firmware program. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having a distributed ledger that tokenizes aninstruction set for an FPGA, such that operation on the distributedledger provides provable access to the FPGA. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having a distributed ledger that tokenizesserverless code logic, such that operation on the distributed ledgerprovides provable access to the serverless code logic. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having a distributed ledger that tokenizes aninstruction set for a crystal fabrication system, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically sells its network bandwidth on a forwardmarket for network capacity and having a distributed ledger thattokenizes an instruction set for a food preparation process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a distributed ledger that tokenizes an instruction set for apolymer production process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having a distributed ledger that tokenizes aninstruction set for chemical synthesis process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically sells its network bandwidth on a forwardmarket for network capacity and having a distributed ledger thattokenizes an instruction set for a biological production process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a distributed ledger that tokenizes a trade secret with an expertwrapper, such that operation on the distributed ledger provides provableaccess to the trade secret and the wrapper provides validation of thetrade secret by the expert. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a distributed ledger that aggregates views of a trade secret intoa chain that proves which and how many parties have viewed the tradesecret. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically sells its network bandwidth on aforward market for network capacity and having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set and execution ofthe instruction set on a system results in recording a transaction inthe distributed ledger. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a distributed ledger that tokenizes an item of intellectualproperty and a reporting system that reports an analytic result based onthe operations performed on the distributed ledger or the intellectualproperty. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its network bandwidthon a forward market for network capacity and having a distributed ledgerthat aggregates a set of instructions, where an operation on thedistributed ledger adds at least one instruction to a pre-existing setof instructions to provide a modified set of instructions. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its network bandwidth on a forwardmarket for network capacity and having a smart wrapper for management ofa distributed ledger that aggregates sets of instructions, where thesmart wrapper manages allocation of instruction sub-sets to thedistributed ledger and access to the instruction sub-sets. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its network bandwidth on a forwardmarket for network capacity and having a smart wrapper for acryptocurrency coin that directs execution of a transaction involvingthe coin to a geographic location based on tax treatment of at least oneof the coin and the transaction in the geographic location. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its network bandwidth on a forwardmarket for network capacity and having a self-executing cryptocurrencycoin that commits a transaction upon recognizing a location-basedparameter that provides favorable tax treatment. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having an expert system that uses machine learningto optimize the execution of cryptocurrency transactions based on taxstatus. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically sells its network bandwidth on aforward market for network capacity and having an expert system thataggregates regulatory information covering cryptocurrency transactionsand automatically selects a jurisdiction for an operation based on theregulatory information. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving an expert system that uses machine learning to optimize theexecution of a cryptocurrency transaction based on real time energyprice information for an available energy source. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having an expert system that uses machine learningto optimize the execution of a cryptocurrency transaction based on anunderstanding of available energy sources to power computing resourcesto execute the transaction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving an expert system that uses machine learning to optimize chargingand recharging cycle of a rechargeable battery system to provide energyfor execution of a cryptocurrency transaction. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its network bandwidthon a forward market for network capacity and having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its network bandwidthon a forward market for network capacity and having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its network bandwidthon a forward market for network capacity and having an expert systemthat predicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its network bandwidth on a forwardmarket for network capacity and having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having an expert system that predicts a forwardmarket price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its network bandwidth on a forwardmarket for network capacity and having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its network bandwidthon a forward market for network capacity and having an expert systemthat predicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its network bandwidthon a forward market for network capacity and having an expert systemthat predicts a forward market price in a market for advertising basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its network bandwidthon a forward market for network capacity and having an expert systemthat predicts a forward market price in a market for advertising basedon an understanding obtained by analyzing social network data sourcesand executes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its network bandwidth on a forwardmarket for network capacity and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom automated agent behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having a machine that automatically forecastsforward market pricing of energy prices based on information collectedfrom business entity behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom business entity behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having a machine that automatically forecastsforward market pricing of energy prices based on information collectedfrom human behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its network bandwidthon a forward market for network capacity and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its network bandwidth on a forwardmarket for network capacity and having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its network bandwidth on a forwardmarket for network capacity and having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing social data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its network bandwidth on a forwardmarket for network capacity and having an intelligent agent that isconfigured to solicit the attention resources of another externalintelligent agent. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a machine that automatically purchases attention resources in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a fleet of machines that automatically aggregate purchasing in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a likelihoodof a facility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a facilityproduction outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically sells its network bandwidth on a forwardmarket for network capacity and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource outputselection among a set of available outputs. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimizeconfiguration of available energy and compute resources to produce afavorable facility resource configuration profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize selectionand configuration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallysells its network bandwidth on a forward market for network capacity andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically sells its network bandwidth on a forward market fornetwork capacity and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically sells its network bandwidth on a forwardmarket for network capacity and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its network bandwidthon a forward market for network capacity and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having a machine that automatically sellsits network bandwidth on a forward market for network capacity andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a utilization parameter for the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically sells its network bandwidthon a forward market for network capacity and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a fleet of machines that automaticallyoptimize energy utilization for compute task allocation. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a forwardmarket for network spectrum and having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a fleet of machines that automaticallyaggregate data on collective optimization of forward market purchases ofenergy credits. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a fleet of machines that automaticallyaggregate data on collective optimization of forward market purchases ofnetwork spectrum. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a fleet of machines that automaticallyaggregate data on collective optimization of forward market sales ofcompute capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a machine that automatically purchases itsenergy in a spot market for energy. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a machine that automatically purchasesenergy credits in a spot market. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a fleet of machines that automaticallyaggregate purchasing in a spot market for energy. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a forwardmarket for network spectrum and having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically purchase spectrumallocation in a forward market for network spectrum and having a fleetof machines that automatically purchase spectrum allocation in a spotmarket for network spectrum. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a fleet of machines that automaticallyoptimize energy utilization for compute task allocation. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a forwardmarket for network spectrum and having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a fleet of machines that automaticallyaggregate data on collective optimization of spot market purchases ofenergy credits. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a fleet of machines that automaticallyaggregate data on collective optimization of spot market purchases ofnetwork spectrum. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a fleet of machines that automatically selltheir aggregate compute capacity on a forward market for computecapacity. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically purchase spectrumallocation in a forward market for network spectrum and having a fleetof machines that automatically sell their aggregate compute storagecapacity on a forward market for storage capacity. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a forwardmarket for network spectrum and having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a fleet of machines that automatically selltheir aggregate network bandwidth on a forward market for networkcapacity. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically purchase spectrumallocation in a forward market for network spectrum and having a machinethat automatically forecasts forward market pricing of energy pricesbased on information collected from social media data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a forwardmarket for network spectrum and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from social media data sources. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a machine that automatically executes anarbitrage strategy for purchase or sale of compute capacity by testing aspot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically purchase spectrumallocation in a forward market for network spectrum and having a machinethat automatically executes an arbitrage strategy for purchase or saleof energy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a machine that automatically executes anarbitrage strategy for purchase or sale of network spectrum or bandwidthby testing a spot market for compute capacity with a small transactionand rapidly executing a larger transaction based on the outcome of thesmall transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a machine that automatically executes anarbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically purchase spectrumallocation in a forward market for network spectrum and having a machinethat automatically executes an arbitrage strategy for purchase or saleof energy credits by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a machine that automatically allocates itsenergy capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a forwardmarket for network spectrum and having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a machine thatautomatically allocates its networking capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a fleet of machines that automaticallyallocate collective energy capacity among a core task, a compute task,an energy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a fleet of machines that automaticallyallocate collective networking capacity among a core task, a computetask, an energy storage task, a data storage task and a networking task.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a smart contract wrapperusing a distributed ledger wherein the smart contract embeds IPlicensing terms for intellectual property embedded in the distributedledger and wherein executing an operation on the distributed ledgerprovides access to the intellectual property and commits the executingparty to the IP licensing terms. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to agree to an apportionment ofroyalties among the parties in the ledger. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to commit a party to a contract term. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a distributed ledger that tokenizes aninstruction set, such that operation on the distributed ledger providesprovable access to the instruction set. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a distributed ledger that tokenizesexecutable algorithmic logic, such that operation on the distributedledger provides provable access to the executable algorithmic logic. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a distributed ledger thattokenizes a 3D printer instruction set, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a distributed ledger thattokenizes an instruction set for a coating process, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically purchase spectrumallocation in a forward market for network spectrum and having adistributed ledger that tokenizes an instruction set for a semiconductorfabrication process, such that operation on the distributed ledgerprovides provable access to the fabrication process. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a forwardmarket for network spectrum and having a distributed ledger thattokenizes a firmware program, such that operation on the distributedledger provides provable access to the firmware program. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a forwardmarket for network spectrum and having a distributed ledger thattokenizes an instruction set for an FPGA, such that operation on thedistributed ledger provides provable access to the FPGA. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a forwardmarket for network spectrum and having a distributed ledger thattokenizes serverless code logic, such that operation on the distributedledger provides provable access to the serverless code logic. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a distributed ledger thattokenizes an instruction set for a crystal fabrication system, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a distributed ledger that tokenizes aninstruction set for a food preparation process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a distributed ledger thattokenizes an instruction set for a polymer production process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a distributed ledger that tokenizes aninstruction set for chemical synthesis process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a distributed ledger thattokenizes an instruction set for a biological production process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a distributed ledger that tokenizes a tradesecret with an expert wrapper, such that operation on the distributedledger provides provable access to the trade secret and the wrapperprovides validation of the trade secret by the expert. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a forwardmarket for network spectrum and having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a distributed ledger that tokenizes aninstruction set, such that operation on the distributed ledger providesprovable access to the instruction set and execution of the instructionset on a system results in recording a transaction in the distributedledger. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically purchase spectrumallocation in a forward market for network spectrum and having adistributed ledger that tokenizes an item of intellectual property and areporting system that reports an analytic result based on the operationsperformed on the distributed ledger or the intellectual property. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a distributed ledger thataggregates a set of instructions, where an operation on the distributedledger adds at least one instruction to a pre-existing set ofinstructions to provide a modified set of instructions. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a forwardmarket for network spectrum and having a smart wrapper for management ofa distributed ledger that aggregates sets of instructions, where thesmart wrapper manages allocation of instruction sub-sets to thedistributed ledger and access to the instruction sub-sets. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a smart wrapper for acryptocurrency coin that directs execution of a transaction involvingthe coin to a geographic location based on tax treatment of at least oneof the coin and the transaction in the geographic location. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having an expert system thatuses machine learning to optimize the execution of cryptocurrencytransactions based on tax status. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having an expert system that aggregates regulatoryinformation covering cryptocurrency transactions and automaticallyselects a jurisdiction for an operation based on the regulatoryinformation. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically purchase spectrumallocation in a forward market for network spectrum and having an expertsystem that uses machine learning to optimize the execution of acryptocurrency transaction based on real time energy price informationfor an available energy source. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having an expert system that uses machine learningto optimize the execution of a cryptocurrency transaction based on anunderstanding of available energy sources to power computing resourcesto execute the transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having an expert system that uses machine learningto optimize charging and recharging cycle of a rechargeable batterysystem to provide energy for execution of a cryptocurrency transaction.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a forwardmarket for network spectrum and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a forwardmarket for network spectrum and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a forwardmarket for network spectrum and having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a forwardmarket for network spectrum and having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a forwardmarket for network spectrum and having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having an expert system that predicts a forwardmarket price in a market for computing resources based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing social datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically purchase spectrumallocation in a forward market for network spectrum and having anintelligent agent that is configured to solicit the attention resourcesof another external intelligent agent. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a machine that automatically purchasesattention resources in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a forwardmarket for network spectrum and having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a likelihood of a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically purchase spectrumallocation in a forward market for network spectrum and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically purchase spectrumallocation in a forward market for network spectrum and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource outputselection among a set of available outputs. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to generate an indication that a current or prospectivecustomer should be contacted about an output that can be provided by thefacility. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically purchase spectrumallocation in a forward market for network spectrum and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically purchase spectrumallocation in a forward market for network spectrum and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of input resources. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aforward market for network spectrum and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to anoutput parameter. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a forward market fornetwork spectrum and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of parameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically optimize energy utilization forcompute task allocation. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of network spectrum.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically optimize energy utilization forcompute task allocation and having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically purchases its energy in a spotmarket for energy. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically purchases energy credits in aspot market. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically optimize energyutilization for compute task allocation and having a fleet of machinesthat automatically aggregate purchasing in a spot market for energy. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically optimize energyutilization for compute task allocation and having a fleet of machinesthat automatically optimize energy utilization for compute taskallocation. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically optimize energyutilization for compute task allocation and having a fleet of machinesthat automatically aggregate data on collective optimization of spotmarket purchases of energy. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy credits. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a fleet of machines that automatically sell their aggregatecompute capacity on a forward market for compute capacity. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a fleet of machines that automatically sell their aggregateenergy storage capacity on a forward market for energy storage capacity.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically optimize energy utilization forcompute task allocation and having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically forecasts forward market pricingof energy prices based on information collected from social media datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically optimize energyutilization for compute task allocation and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically optimize energy utilization for compute taskallocation and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromsocial media data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically forecasts forward market valueof compute capability based on information collected from social mediadata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically optimize energyutilization for compute task allocation and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically executes an arbitrage strategyfor purchase or sale of energy storage capacity by testing a spot marketfor compute capacity with a small transaction and rapidly executing alarger transaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a machine that automatically executesan arbitrage strategy for purchase or sale of network spectrum orbandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically executes an arbitrage strategyfor purchase or sale of energy by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically optimize energyutilization for compute task allocation and having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking task.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically optimize energy utilization forcompute task allocation and having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a fleet of machines that automatically allocate collectivecompute capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically optimize energy utilization for compute taskallocation and having a fleet of machines that automatically allocatecollective networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a smart contract wrapper using adistributed ledger wherein the smart contract embeds IP licensing termsfor intellectual property embedded in the distributed ledger and whereinexecuting an operation on the distributed ledger provides access to theintellectual property and commits the executing party to the IPlicensing terms. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toan aggregate stack of intellectual property. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toagree to an apportionment of royalties among the parties in the ledger.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically optimize energy utilization forcompute task allocation and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to commit aparty to a contract term. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger that tokenizes an instruction set, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger that tokenizes executable algorithmiclogic, such that operation on the distributed ledger provides provableaccess to the executable algorithmic logic. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger that tokenizes a 3D printer instructionset, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger that tokenizes an instruction set for acoating process, such that operation on the distributed ledger providesprovable access to the instruction set. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger that tokenizes an instruction set for asemiconductor fabrication process, such that operation on thedistributed ledger provides provable access to the fabrication process.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically optimize energy utilization forcompute task allocation and having a distributed ledger that tokenizes afirmware program, such that operation on the distributed ledger providesprovable access to the firmware program. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger that tokenizes an instruction set for anFPGA, such that operation on the distributed ledger provides provableaccess to the FPGA. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger that tokenizes serverless code logic,such that operation on the distributed ledger provides provable accessto the serverless code logic. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger that tokenizes an instruction set for acrystal fabrication system, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically optimize energy utilization for compute taskallocation and having a distributed ledger that tokenizes an instructionset for a food preparation process, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a distributed ledger that tokenizesan instruction set for a polymer production process, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically optimize energyutilization for compute task allocation and having a distributed ledgerthat tokenizes an instruction set for chemical synthesis process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger that tokenizes an instruction set for abiological production process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically optimize energy utilization for compute taskallocation and having a distributed ledger that tokenizes a trade secretwith an expert wrapper, such that operation on the distributed ledgerprovides provable access to the trade secret and the wrapper providesvalidation of the trade secret by the expert. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger that aggregates views of a trade secretinto a chain that proves which and how many parties have viewed thetrade secret. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically optimize energyutilization for compute task allocation and having a distributed ledgerthat tokenizes an instruction set, such that operation on thedistributed ledger provides provable access to the instruction set andexecution of the instruction set on a system results in recording atransaction in the distributed ledger. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger that tokenizes an item of intellectualproperty and a reporting system that reports an analytic result based onthe operations performed on the distributed ledger or the intellectualproperty. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically optimize energyutilization for compute task allocation and having a distributed ledgerthat aggregates a set of instructions, where an operation on thedistributed ledger adds at least one instruction to a pre-existing setof instructions to provide a modified set of instructions. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a smart wrapper for management of adistributed ledger that aggregates sets of instructions, where the smartwrapper manages allocation of instruction sub-sets to the distributedledger and access to the instruction sub-sets. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a self-executing cryptocurrency coin that commits atransaction upon recognizing a location-based parameter that providesfavorable tax treatment. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having an expert system that uses machine learning to optimize theexecution of cryptocurrency transactions based on tax status. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having an expert system that uses machine learning to optimize theexecution of a cryptocurrency transaction based on real time energyprice information for an available energy source. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically optimize energy utilization for compute taskallocation and having an expert system that uses machine learning tooptimize the execution of a cryptocurrency transaction based on anunderstanding of available energy sources to power computing resourcesto execute the transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically optimize energyutilization for compute task allocation and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically optimize energy utilization for compute taskallocation and having an expert system that predicts a forward marketprice in an energy market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having an expert system that predicts a forward market price in anenergy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having an expert system that predicts a forward market price in amarket for computing resources based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically optimize energy utilization for compute taskallocation and having an expert system that predicts a forward marketprice in a market for computing resources based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically optimize energy utilization for compute taskallocation and having an expert system that predicts a forward marketprice in a market for advertising based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically forecasts forward market pricingof energy prices based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically forecasts forward market pricingof network spectrum based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically forecasts forward market pricingof energy credits based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically forecasts forward market valueof compute capability based on information collected from automatedagent behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically forecasts forward market pricingof energy prices based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically forecasts forward market pricingof network spectrum based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically forecasts forward market pricingof energy credits based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically forecasts forward market valueof compute capability based on information collected from businessentity behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically forecasts forward market pricingof energy prices based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically optimize energyutilization for compute task allocation and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically forecasts forward market valueof compute capability based on information collected from humanbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing social data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having an intelligent agent that is configured to solicit theattention resources of another external intelligent agent. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a machine that automaticallypurchases attention resources in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto predict a likelihood of a facility production outcome. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto predict a facility production outcome. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically optimize energy utilization forcompute task allocation and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize provisioning and allocation of energy and compute resourcesto produce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize requisitionand provisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically optimize energyutilization for compute task allocation and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize selectionand configuration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically optimize energy utilization for compute taskallocation and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically optimize energy utilization for compute taskallocation and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically optimize energyutilization for compute task allocation and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofdetected conditions relating to a utilization parameter for the outputof the facility. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofparameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy and having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a machine that automaticallypurchases its energy in a spot market for energy. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy and having a machine thatautomatically purchases energy credits in a spot market. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy and having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy and having a fleet ofmachines that automatically aggregate purchasing energy credits in aspot market. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy and havinga machine that automatically purchases spectrum allocation in a spotmarket for network spectrum. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy and havinga fleet of machines that automatically optimize energy utilization forcompute task allocation. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy storagecapacity by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of network spectrumor bandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy and havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy credits by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy and having a machinethat automatically allocates its compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy and having a fleet ofmachines that automatically allocate collective energy capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a smart contract wrapper using adistributed ledger wherein the smart contract embeds IP licensing termsfor intellectual property embedded in the distributed ledger and whereinexecuting an operation on the distributed ledger provides access to theintellectual property and commits the executing party to the IPlicensing terms. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to agree to an apportionment ofroyalties among the parties in the ledger. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to commit a party to a contract term. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy and having a distributed ledger thattokenizes executable algorithmic logic, such that operation on thedistributed ledger provides provable access to the executablealgorithmic logic. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a distributed ledger thattokenizes a 3D printer instruction set, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy and having adistributed ledger that tokenizes an instruction set for a coatingprocess, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a distributed ledger thattokenizes an instruction set for a semiconductor fabrication process,such that operation on the distributed ledger provides provable accessto the fabrication process. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a distributed ledger thattokenizes a firmware program, such that operation on the distributedledger provides provable access to the firmware program. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy and having a distributed ledger thattokenizes an instruction set for an FPGA, such that operation on thedistributed ledger provides provable access to the FPGA. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy and having a distributed ledger thattokenizes serverless code logic, such that operation on the distributedledger provides provable access to the serverless code logic. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy and having adistributed ledger that tokenizes an instruction set for a crystalfabrication system, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy and having a distributed ledger thattokenizes an instruction set for a food preparation process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a distributed ledger thattokenizes an instruction set for a polymer production process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a distributed ledger thattokenizes an instruction set for chemical synthesis process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a distributed ledger thattokenizes an instruction set for a biological production process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a distributed ledger thattokenizes a trade secret with an expert wrapper, such that operation onthe distributed ledger provides provable access to the trade secret andthe wrapper provides validation of the trade secret by the expert. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy and having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy and having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy and having a distributed ledger thattokenizes an item of intellectual property and a reporting system thatreports an analytic result based on the operations performed on thedistributed ledger or the intellectual property. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy and having a distributed ledger thataggregates a set of instructions, where an operation on the distributedledger adds at least one instruction to a pre-existing set ofinstructions to provide a modified set of instructions. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy and having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy and having a smartwrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a self-executing cryptocurrencycoin that commits a transaction upon recognizing a location-basedparameter that provides favorable tax treatment. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy and having an expert system that usesmachine learning to optimize the execution of cryptocurrencytransactions based on tax status. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having an expert system that uses machinelearning to optimize the execution of a cryptocurrency transaction basedon real time energy price information for an available energy source. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy and having an expertsystem that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy and having an expertsystem that uses machine learning to optimize charging and rechargingcycle of a rechargeable battery system to provide energy for executionof a cryptocurrency transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing social network data sources and executes acryptocurrency transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy and having an expertsystem that predicts a forward market price in an energy market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy and having an expertsystem that predicts a forward market price in an energy market based onan understanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy and having an expertsystem that predicts a forward market price in a market for computingresources based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy and havingan expert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy and having an expertsystem that predicts a forward market price in a market for advertisingbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy and havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy and havinga machine that automatically forecasts forward market pricing of networkspectrum based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy and havinga machine that automatically forecasts forward market pricing of energycredits based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy and havinga machine that automatically forecasts forward market value of computecapability based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy and having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from business entity behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy and havinga machine that automatically forecasts forward market value of computecapability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy and having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy and having a machinethat automatically forecasts forward market value of compute capabilitybased on information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy and having an expertsystem that predicts a forward market price in a market for spectrum ornetwork bandwidth based on an understanding obtained by analyzing socialdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy and havingan intelligent agent that is configured to solicit the attentionresources of another external intelligent agent. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy and having a machine thatautomatically purchases attention resources in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy and havinga fleet of machines that automatically aggregate purchasing in a forwardmarket for attention. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a likelihood of a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize requisition andprovisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to generate an indication that a current or prospectivecustomer should be contacted about an output that can be provided by thefacility. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to at least one of an input resource, a facilityresource, an output parameter and an external condition related to theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of inputresources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of facility resources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to anoutput parameter. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a utilizationparameter for the output of the facility. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of parameters received from a digital twin for thefacility.

In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having afleet of machines that automatically aggregate data on collectiveoptimization of forward market sales of compute capacity. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having amachine that automatically purchases its energy in a spot market forenergy. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy creditsand having a machine that automatically purchases energy credits in aspot market. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy creditsand having a fleet of machines that automatically aggregate purchasingin a spot market for energy. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having amachine that automatically purchases spectrum allocation in a spotmarket for network spectrum. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy creditsand having a fleet of machines that automatically optimize energyutilization for compute task allocation. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy credits and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy credits and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy credits and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy credits and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy credits by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking task.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having amachine that automatically allocates its compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a machine thatautomatically allocates its networking capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a smart contract wrapperusing a distributed ledger wherein the smart contract embeds IPlicensing terms for intellectual property embedded in the distributedledger and wherein executing an operation on the distributed ledgerprovides access to the intellectual property and commits the executingparty to the IP licensing terms. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to agree to an apportionment ofroyalties among the parties in the ledger. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to commit a party to a contract term. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy credits and having a distributedledger that tokenizes executable algorithmic logic, such that operationon the distributed ledger provides provable access to the executablealgorithmic logic. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a distributed ledger thattokenizes a 3D printer instruction set, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having adistributed ledger that tokenizes an instruction set for a coatingprocess, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a distributed ledger thattokenizes an instruction set for a semiconductor fabrication process,such that operation on the distributed ledger provides provable accessto the fabrication process. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a distributed ledger thattokenizes a firmware program, such that operation on the distributedledger provides provable access to the firmware program. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy credits and having a distributedledger that tokenizes an instruction set for an FPGA, such thatoperation on the distributed ledger provides provable access to theFPGA. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy creditsand having a distributed ledger that tokenizes serverless code logic,such that operation on the distributed ledger provides provable accessto the serverless code logic. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a distributed ledger thattokenizes an instruction set for a crystal fabrication system, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a distributed ledger thattokenizes an instruction set for a food preparation process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a distributed ledger thattokenizes an instruction set for a polymer production process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a distributed ledger thattokenizes an instruction set for chemical synthesis process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a distributed ledger thattokenizes an instruction set for a biological production process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a distributed ledger thattokenizes a trade secret with an expert wrapper, such that operation onthe distributed ledger provides provable access to the trade secret andthe wrapper provides validation of the trade secret by the expert. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy credits and having a distributedledger that tokenizes an item of intellectual property and a reportingsystem that reports an analytic result based on the operations performedon the distributed ledger or the intellectual property. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy credits and having a distributedledger that aggregates a set of instructions, where an operation on thedistributed ledger adds at least one instruction to a pre-existing setof instructions to provide a modified set of instructions. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having asmart wrapper for management of a distributed ledger that aggregatessets of instructions, where the smart wrapper manages allocation ofinstruction sub-sets to the distributed ledger and access to theinstruction sub-sets. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a smart wrapper for acryptocurrency coin that directs execution of a transaction involvingthe coin to a geographic location based on tax treatment of at least oneof the coin and the transaction in the geographic location. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having aself-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy creditsand having an expert system that uses machine learning to optimize theexecution of cryptocurrency transactions based on tax status. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having anexpert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy credits and having an expert systemthat uses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy creditsand having an expert system that uses machine learning to optimize theexecution of a cryptocurrency transaction based on an understanding ofavailable energy sources to power computing resources to execute thetransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy creditsand having an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy creditsand having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a cryptocurrency transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy creditsand having an expert system that predicts a forward market price in amarket for computing resources based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy creditsand having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy creditsand having a machine that automatically forecasts forward market pricingof network spectrum based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having amachine that automatically forecasts forward market value of computecapability based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy creditsand having a machine that automatically forecasts forward market pricingof energy credits based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy creditsand having a machine that automatically forecasts forward market pricingof network spectrum based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy creditsand having a machine that automatically forecasts forward market pricingof energy credits based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy creditsand having a machine that automatically forecasts forward market valueof compute capability based on information collected from humanbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing social datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy creditsand having an intelligent agent that is configured to solicit theattention resources of another external intelligent agent. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having amachine that automatically purchases attention resources in a forwardmarket for attention. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a facility production outcome. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize requisition andprovisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of energy creditsand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimizeconfiguration of available energy and compute resources to produce afavorable facility resource configuration profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize selection and configuration of an artificialintelligence system to produce a favorable facility output profile amonga set of available artificial intelligence systems and configurations.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to atleast one of an input resource, a facility resource, an output parameterand an external condition related to the output of the facility. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of energy credits and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of input resources. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to a setof facility resources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to anoutput parameter. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of energy credits and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to autilization parameter for the output of the facility. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of energy credits and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum and havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market sales of compute capacity. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum and havinga machine that automatically purchases its energy in a spot market forenergy. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of network spectrumand having a machine that automatically purchases energy credits in aspot market. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of network spectrumand having a fleet of machines that automatically aggregate purchasingin a spot market for energy. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum and havinga machine that automatically purchases spectrum allocation in a spotmarket for network spectrum. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of network spectrumand having a fleet of machines that automatically optimize energyutilization for compute task allocation. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of network spectrum and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of network spectrum and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of network spectrum and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of network spectrum and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum and havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy credits by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking task.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum and havinga machine that automatically allocates its compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a machine thatautomatically allocates its networking capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a smart contract wrapperusing a distributed ledger wherein the smart contract embeds IPlicensing terms for intellectual property embedded in the distributedledger and wherein executing an operation on the distributed ledgerprovides access to the intellectual property and commits the executingparty to the IP licensing terms. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to agree to an apportionment ofroyalties among the parties in the ledger. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to commit a party to a contract term. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a distributed ledgerthat tokenizes an instruction set, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum and havinga distributed ledger that tokenizes executable algorithmic logic, suchthat operation on the distributed ledger provides provable access to theexecutable algorithmic logic. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a distributed ledgerthat tokenizes a 3D printer instruction set, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum and havinga distributed ledger that tokenizes an instruction set for a coatingprocess, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a distributed ledgerthat tokenizes an instruction set for a semiconductor fabricationprocess, such that operation on the distributed ledger provides provableaccess to the fabrication process. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a distributed ledgerthat tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum and havinga distributed ledger that tokenizes an instruction set for an FPGA, suchthat operation on the distributed ledger provides provable access to theFPGA. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of network spectrumand having a distributed ledger that tokenizes serverless code logic,such that operation on the distributed ledger provides provable accessto the serverless code logic. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a distributed ledgerthat tokenizes an instruction set for a crystal fabrication system, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a distributed ledgerthat tokenizes an instruction set for a food preparation process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a distributed ledgerthat tokenizes an instruction set for a polymer production process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a distributed ledgerthat tokenizes an instruction set for chemical synthesis process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a distributed ledgerthat tokenizes an instruction set for a biological production process,such that operation on the distributed ledger provides provable accessto the instruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a distributed ledgerthat tokenizes a trade secret with an expert wrapper, such thatoperation on the distributed ledger provides provable access to thetrade secret and the wrapper provides validation of the trade secret bythe expert. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of network spectrumand having a distributed ledger that aggregates views of a trade secretinto a chain that proves which and how many parties have viewed thetrade secret. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of network spectrumand having a distributed ledger that tokenizes an instruction set, suchthat operation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of network spectrum and having a distributedledger that tokenizes an item of intellectual property and a reportingsystem that reports an analytic result based on the operations performedon the distributed ledger or the intellectual property. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of network spectrum and having a distributedledger that aggregates a set of instructions, where an operation on thedistributed ledger adds at least one instruction to a pre-existing setof instructions to provide a modified set of instructions. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum and havinga smart wrapper for management of a distributed ledger that aggregatessets of instructions, where the smart wrapper manages allocation ofinstruction sub-sets to the distributed ledger and access to theinstruction sub-sets. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a smart wrapper for acryptocurrency coin that directs execution of a transaction involvingthe coin to a geographic location based on tax treatment of at least oneof the coin and the transaction in the geographic location. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum and havinga self-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of network spectrumand having an expert system that uses machine learning to optimize theexecution of cryptocurrency transactions based on tax status. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum and havingan expert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of network spectrum and having an expert systemthat uses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of network spectrumand having an expert system that uses machine learning to optimize theexecution of a cryptocurrency transaction based on an understanding ofavailable energy sources to power computing resources to execute thetransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of network spectrumand having an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum and havingan expert system that predicts a forward market price in a market basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of network spectrumand having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of network spectrumand having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a cryptocurrency transaction based onthe forward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing social network data sources and executes acryptocurrency transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum and havingan expert system that predicts a forward market price in an energymarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum and havingan expert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of network spectrumand having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum and havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of network spectrumand having a machine that automatically forecasts forward market pricingof network spectrum based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum and havinga machine that automatically forecasts forward market value of computecapability based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum and havinga machine that automatically forecasts forward market pricing of networkspectrum based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of network spectrumand having a machine that automatically forecasts forward market pricingof energy credits based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum and havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of network spectrumand having a machine that automatically forecasts forward market pricingof network spectrum based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of network spectrumand having a machine that automatically forecasts forward market pricingof energy credits based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of network spectrumand having a machine that automatically forecasts forward market valueof compute capability based on information collected from humanbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing social datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of network spectrumand having an intelligent agent that is configured to solicit theattention resources of another external intelligent agent. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum and havinga machine that automatically purchases attention resources in a forwardmarket for attention. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a facility production outcome. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize requisition andprovisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market purchases of network spectrumand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimizeconfiguration of available energy and compute resources to produce afavorable facility resource configuration profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize selection and configuration of an artificialintelligence system to produce a favorable facility output profile amonga set of available artificial intelligence systems and configurations.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum and havinga system for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to atleast one of an input resource, a facility resource, an output parameterand an external condition related to the output of the facility. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market purchases of network spectrum and havingan intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of input resources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of network spectrum and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of facility resources. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to anoutput parameter. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket purchases of network spectrum and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to autilization parameter for the output of the facility. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market purchases of network spectrum and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market sales of compute capacity. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market sales of compute capacity and having amachine that automatically purchases its energy in a spot market forenergy. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically aggregate data oncollective optimization of forward market sales of compute capacity andhaving a machine that automatically purchases energy credits in a spotmarket. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically aggregate data oncollective optimization of forward market sales of compute capacity andhaving a fleet of machines that automatically aggregate purchasing in aspot market for energy. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market sales of compute capacity and having amachine that automatically purchases spectrum allocation in a spotmarket for network spectrum. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market sales of compute capacity andhaving a fleet of machines that automatically optimize energyutilization for compute task allocation. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market sales of compute capacity and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market sales of compute capacity and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market sales of compute capacity and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy storagecapacity by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of network spectrumor bandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market sales of compute capacity andhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of energy credits by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market sales of compute capacity and having amachine that automatically allocates its energy capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market sales of compute capacity and having amachine that automatically allocates its networking capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a smart contract wrapperusing a distributed ledger wherein the smart contract embeds IPlicensing terms for intellectual property embedded in the distributedledger and wherein executing an operation on the distributed ledgerprovides access to the intellectual property and commits the executingparty to the IP licensing terms. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to agree to an apportionment ofroyalties among the parties in the ledger. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to commit a party to a contract term. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market sales of compute capacity and having a distributed ledgerthat tokenizes executable algorithmic logic, such that operation on thedistributed ledger provides provable access to the executablealgorithmic logic. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a distributed ledger thattokenizes a 3D printer instruction set, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market sales of compute capacity and having adistributed ledger that tokenizes an instruction set for a coatingprocess, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a distributed ledger thattokenizes an instruction set for a semiconductor fabrication process,such that operation on the distributed ledger provides provable accessto the fabrication process. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a distributed ledger thattokenizes a firmware program, such that operation on the distributedledger provides provable access to the firmware program. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market sales of compute capacity and having a distributed ledgerthat tokenizes an instruction set for an FPGA, such that operation onthe distributed ledger provides provable access to the FPGA. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market sales of compute capacity and having adistributed ledger that tokenizes serverless code logic, such thatoperation on the distributed ledger provides provable access to theserverless code logic. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a distributed ledger thattokenizes an instruction set for a crystal fabrication system, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a distributed ledger thattokenizes an instruction set for a food preparation process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a distributed ledger thattokenizes an instruction set for a polymer production process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a distributed ledger thattokenizes an instruction set for chemical synthesis process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a distributed ledger thattokenizes an instruction set for a biological production process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a distributed ledger thattokenizes a trade secret with an expert wrapper, such that operation onthe distributed ledger provides provable access to the trade secret andthe wrapper provides validation of the trade secret by the expert. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market sales of compute capacity and having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market sales of compute capacity and having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market sales of compute capacity and having a distributed ledgerthat tokenizes an item of intellectual property and a reporting systemthat reports an analytic result based on the operations performed on thedistributed ledger or the intellectual property. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market sales of compute capacity and having a distributed ledgerthat aggregates a set of instructions, where an operation on thedistributed ledger adds at least one instruction to a pre-existing setof instructions to provide a modified set of instructions. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market sales of compute capacity and having asmart wrapper for management of a distributed ledger that aggregatessets of instructions, where the smart wrapper manages allocation ofinstruction sub-sets to the distributed ledger and access to theinstruction sub-sets. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a smart wrapper for acryptocurrency coin that directs execution of a transaction involvingthe coin to a geographic location based on tax treatment of at least oneof the coin and the transaction in the geographic location. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market sales of compute capacity and having aself-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market sales of compute capacity andhaving an expert system that uses machine learning to optimize theexecution of cryptocurrency transactions based on tax status. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market sales of compute capacity and having anexpert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market sales of compute capacity and having an expert systemthat uses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market sales of compute capacity andhaving an expert system that uses machine learning to optimize theexecution of a cryptocurrency transaction based on an understanding ofavailable energy sources to power computing resources to execute thetransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market sales of compute capacity andhaving an expert system that uses machine learning to optimize chargingand recharging cycle of a rechargeable battery system to provide energyfor execution of a cryptocurrency transaction. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market sales of compute capacity and having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market sales of compute capacity and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market sales of compute capacity andhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing social network datasources and executes a cryptocurrency transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market sales of compute capacity and having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market sales of compute capacity andhaving an expert system that predicts a forward market price in a marketfor computing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market sales of compute capacity andhaving an expert system that predicts a forward market price in a marketfor advertising based on an understanding obtained by analyzing Internetof Things data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market sales of compute capacity and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market sales of compute capacity andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market sales of compute capacity and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market sales of compute capacity and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market sales of compute capacity andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market sales of compute capacity and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of forward market sales of compute capacity and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market sales of compute capacity andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market sales of compute capacity andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market sales of compute capacity andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market sales of compute capacity andhaving an expert system that predicts a forward market price in a marketfor spectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having an intelligent agent that isconfigured to solicit the attention resources of another externalintelligent agent. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a machine that automaticallypurchases attention resources in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market sales of compute capacity and having afleet of machines that automatically aggregate purchasing in a forwardmarket for attention. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a likelihood of a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market sales of compute capacity andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a facilityproduction outcome. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource utilizationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource outputselection among a set of available outputs. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize requisition and provisioning of availableenergy and compute resources to produce a favorable facility inputresource profile among a set of available profiles. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market sales of compute capacity and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of forward market sales of compute capacity and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to generate an indication that a current or prospectivecustomer should be contacted about an output that can be provided by thefacility. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of forward market sales of compute capacity andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to at least one of an input resource, a facilityresource, an output parameter and an external condition related to theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to a setof input resources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to a setof facility resources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to anoutput parameter. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of forwardmarket sales of compute capacity and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to autilization parameter for the output of the facility. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization offorward market sales of compute capacity and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases its energy in a spot market forenergy. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically purchases its energy in a spotmarket for energy and having a machine that automatically purchasesenergy credits in a spot market. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having a fleet ofmachines that automatically aggregate purchasing in a spot market forenergy. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically purchases its energy in a spotmarket for energy and having a fleet of machines that automaticallyaggregate purchasing energy credits in a spot market. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a spot market for energy andhaving a machine that automatically purchases spectrum allocation in aspot market for network spectrum. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having a fleet ofmachines that automatically purchase spectrum allocation in a spotmarket for network spectrum. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having a fleet ofmachines that automatically optimize energy utilization for compute taskallocation. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aspot market for energy and having a fleet of machines that automaticallyaggregate data on collective optimization of spot market purchases ofenergy. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically purchases its energy in a spotmarket for energy and having a fleet of machines that automaticallyaggregate data on collective optimization of spot market purchases ofenergy credits. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of network spectrum. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases its energy in a spot market for energy andhaving a fleet of machines that automatically sell their aggregatecompute capacity on a forward market for compute capacity. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a spot market forenergy and having a fleet of machines that automatically sell theiraggregate compute storage capacity on a forward market for storagecapacity. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aspot market for energy and having a fleet of machines that automaticallysell their aggregate energy storage capacity on a forward market forenergy storage capacity. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having a fleet ofmachines that automatically sell their aggregate network bandwidth on aforward market for network capacity. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having a machinethat automatically forecasts forward market pricing of energy pricesbased on information collected from social media data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a spot market forenergy and having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from socialmedia data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from social media data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a spot market forenergy and having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from socialmedia data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having a machinethat automatically executes an arbitrage strategy for purchase or saleof compute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having a machinethat automatically executes an arbitrage strategy for purchase or saleof energy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases its energy in a spot market for energy andhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of network spectrum or bandwidth by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aspot market for energy and having a machine that automatically executesan arbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aspot market for energy and having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aspot market for energy and having a machine that automatically allocatesits energy capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a spot market for energy andhaving a machine that automatically allocates its compute capacity amonga core task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having a machinethat automatically allocates its networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having a fleet ofmachines that automatically allocate collective energy capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having a fleet ofmachines that automatically allocate collective compute capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having a fleet ofmachines that automatically allocate collective networking capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having a smartcontract wrapper using a distributed ledger wherein the smart contractembeds IP licensing terms for intellectual property embedded in thedistributed ledger and wherein executing an operation on the distributedledger provides access to the intellectual property and commits theexecuting party to the IP licensing terms. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases its energy in a spot market for energy andhaving a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toan aggregate stack of intellectual property. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases its energy in a spot market for energy andhaving a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toagree to an apportionment of royalties among the parties in the ledger.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases its energy in a spot market forenergy and having a distributed ledger for aggregating intellectualproperty licensing terms, wherein a smart contract wrapper on thedistributed ledger allows an operation on the ledger to add intellectualproperty to an aggregate stack of intellectual property. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a spot market for energy andhaving a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to commit a party to a contractterm. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically purchases its energy in a spotmarket for energy and having a distributed ledger that tokenizes aninstruction set, such that operation on the distributed ledger providesprovable access to the instruction set. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having adistributed ledger that tokenizes executable algorithmic logic, suchthat operation on the distributed ledger provides provable access to theexecutable algorithmic logic. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having adistributed ledger that tokenizes a 3D printer instruction set, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having adistributed ledger that tokenizes an instruction set for a coatingprocess, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having adistributed ledger that tokenizes an instruction set for a semiconductorfabrication process, such that operation on the distributed ledgerprovides provable access to the fabrication process. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a spot market for energy andhaving a distributed ledger that tokenizes a firmware program, such thatoperation on the distributed ledger provides provable access to thefirmware program. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having adistributed ledger that tokenizes an instruction set for an FPGA, suchthat operation on the distributed ledger provides provable access to theFPGA. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically purchases its energy in a spotmarket for energy and having a distributed ledger that tokenizesserverless code logic, such that operation on the distributed ledgerprovides provable access to the serverless code logic. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a spot market for energy andhaving a distributed ledger that tokenizes an instruction set for acrystal fabrication system, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a spot market for energy andhaving a distributed ledger that tokenizes an instruction set for a foodpreparation process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a spot market for energy andhaving a distributed ledger that tokenizes an instruction set for apolymer production process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a spot market for energy andhaving a distributed ledger that tokenizes an instruction set forchemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a spot market for energy andhaving a distributed ledger that tokenizes an instruction set for abiological production process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a spot market for energy andhaving a distributed ledger that tokenizes a trade secret with an expertwrapper, such that operation on the distributed ledger provides provableaccess to the trade secret and the wrapper provides validation of thetrade secret by the expert. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a spot market forenergy and having a distributed ledger that tokenizes an instructionset, such that operation on the distributed ledger provides provableaccess to the instruction set and execution of the instruction set on asystem results in recording a transaction in the distributed ledger. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a spot market forenergy and having a distributed ledger that tokenizes an item ofintellectual property and a reporting system that reports an analyticresult based on the operations performed on the distributed ledger orthe intellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having adistributed ledger that aggregates a set of instructions, where anoperation on the distributed ledger adds at least one instruction to apre-existing set of instructions to provide a modified set ofinstructions. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aspot market for energy and having a smart wrapper for management of adistributed ledger that aggregates sets of instructions, where the smartwrapper manages allocation of instruction sub-sets to the distributedledger and access to the instruction sub-sets. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases its energy in a spot market for energy andhaving a smart wrapper for a cryptocurrency coin that directs executionof a transaction involving the coin to a geographic location based ontax treatment of at least one of the coin and the transaction in thegeographic location. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having aself-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aspot market for energy and having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aspot market for energy and having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having an expertsystem that uses machine learning to optimize the execution of acryptocurrency transaction based on real time energy price informationfor an available energy source. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having an expertsystem that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a spot market forenergy and having an expert system that uses machine learning tooptimize charging and recharging cycle of a rechargeable battery systemto provide energy for execution of a cryptocurrency transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a spot market forenergy and having an expert system that predicts a forward market pricein a market based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having an expertsystem that predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a spot market forenergy and having an expert system that predicts a forward market pricein a market based on an understanding obtained by analyzing Internet ofThings data sources and executes a cryptocurrency transaction based onthe forward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having an expertsystem that predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aspot market for energy and having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a spot market for energy andhaving an expert system that predicts a forward market price in anenergy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having an expertsystem that predicts a forward market price in a market for computingresources based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having an expertsystem that predicts a forward market price in a market for spectrum ornetwork bandwidth based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having an expertsystem that predicts a forward market price in a market for computingresources based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aspot market for energy and having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a spot market forenergy and having an expert system that predicts a forward market pricein a market for advertising based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having a machinethat automatically forecasts forward market pricing of energy pricesbased on information collected from automated agent behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aspot market for energy and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a spot market for energy andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having a machinethat automatically forecasts forward market value of compute capabilitybased on information collected from automated agent behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aspot market for energy and having a machine that automatically forecastsforward market pricing of energy prices based on information collectedfrom business entity behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases its energy in a spot market for energy andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from business entity behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aspot market for energy and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a spot market for energy andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aspot market for energy and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases its energy in a spot market for energy andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aspot market for energy and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases its energy in a spot market for energy andhaving an expert system that predicts a forward market price in a marketfor spectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having anintelligent agent that is configured to solicit the attention resourcesof another external intelligent agent. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having a machinethat automatically purchases attention resources in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aspot market for energy and having a fleet of machines that automaticallyaggregate purchasing in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a spot market for energy andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a likelihoodof a facility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases its energy in aspot market for energy and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize provisioning and allocation of energy and compute resourcesto produce a favorable facility resource utilization profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a spot market forenergy and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizerequisition and provisioning of available energy and compute resourcesto produce a favorable facility input resource profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a spot market forenergy and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizeselection and configuration of an artificial intelligence system toproduce a favorable facility output profile among a set of availableartificial intelligence systems and configurations. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a spot market for energy andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases its energy in a spot market for energy andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to at least one of an input resource, a facilityresource, an output parameter and an external condition related to theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of input resources. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of facility resources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases its energy in a spot market for energy and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases its energy in a spot market forenergy and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofparameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases energy credits in a spot market.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases energy credits in a spot marketand having a fleet of machines that automatically aggregate purchasingin a spot market for energy. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a fleet of machinesthat automatically aggregate purchasing energy credits in a spot market.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases energy credits in a spot marketand having a machine that automatically purchases spectrum allocation ina spot market for network spectrum. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a fleet of machinesthat automatically purchase spectrum allocation in a spot market fornetwork spectrum. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a fleet of machinesthat automatically optimize energy utilization for compute taskallocation. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aspot market and having a fleet of machines that automatically aggregatedata on collective optimization of spot market purchases of energy. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a spot market andhaving a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy credits. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a spot market andhaving a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of network spectrum. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a spot market andhaving a fleet of machines that automatically sell their aggregatecompute capacity on a forward market for compute capacity. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a spot market andhaving a fleet of machines that automatically sell their aggregatecompute storage capacity on a forward market for storage capacity. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a spot market andhaving a fleet of machines that automatically sell their aggregateenergy storage capacity on a forward market for energy storage capacity.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases energy credits in a spot marketand having a fleet of machines that automatically sell their aggregatenetwork bandwidth on a forward market for network capacity. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a spot market andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from social media datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aspot market and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected fromsocial media data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a spot market and having amachine that automatically forecasts forward market value of computecapability based on information collected from social media datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aspot market and having a machine that automatically executes anarbitrage strategy for purchase or sale of compute capacity by testing aspot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aspot market and having a machine that automatically executes anarbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aspot market and having a machine that automatically executes anarbitrage strategy for purchase or sale of network spectrum or bandwidthby testing a spot market for compute capacity with a small transactionand rapidly executing a larger transaction based on the outcome of thesmall transaction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy credits by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking task.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases energy credits in a spot marketand having a machine that automatically allocates its compute capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a machine thatautomatically allocates its networking capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a fleet of machinesthat automatically allocate collective energy capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a fleet of machinesthat automatically allocate collective compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a fleet of machinesthat automatically allocate collective networking capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a smart contractwrapper using a distributed ledger wherein the smart contract embeds IPlicensing terms for intellectual property embedded in the distributedledger and wherein executing an operation on the distributed ledgerprovides access to the intellectual property and commits the executingparty to the IP licensing terms. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a distributedledger for aggregating intellectual property licensing terms, wherein asmart contract wrapper on the distributed ledger allows an operation onthe ledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a distributedledger for aggregating intellectual property licensing terms, wherein asmart contract wrapper on the distributed ledger allows an operation onthe ledger to add intellectual property to agree to an apportionment ofroyalties among the parties in the ledger. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a spot market and having adistributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to an aggregatestack of intellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a distributedledger for aggregating intellectual property licensing terms, wherein asmart contract wrapper on the distributed ledger allows an operation onthe ledger to commit a party to a contract term. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a spot market and having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a distributedledger that tokenizes executable algorithmic logic, such that operationon the distributed ledger provides provable access to the executablealgorithmic logic. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a distributedledger that tokenizes a 3D printer instruction set, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically purchases energy credits in a spotmarket and having a distributed ledger that tokenizes an instruction setfor a coating process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a spot market and having adistributed ledger that tokenizes an instruction set for a semiconductorfabrication process, such that operation on the distributed ledgerprovides provable access to the fabrication process. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a spot market and having adistributed ledger that tokenizes a firmware program, such thatoperation on the distributed ledger provides provable access to thefirmware program. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a distributedledger that tokenizes an instruction set for an FPGA, such thatoperation on the distributed ledger provides provable access to theFPGA. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically purchases energy credits in a spotmarket and having a distributed ledger that tokenizes serverless codelogic, such that operation on the distributed ledger provides provableaccess to the serverless code logic. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a distributedledger that tokenizes an instruction set for a crystal fabricationsystem, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a distributedledger that tokenizes an instruction set for a food preparation process,such that operation on the distributed ledger provides provable accessto the instruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a distributedledger that tokenizes an instruction set for a polymer productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a distributedledger that tokenizes an instruction set for chemical synthesis process,such that operation on the distributed ledger provides provable accessto the instruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a distributedledger that tokenizes an instruction set for a biological productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a distributedledger that tokenizes a trade secret with an expert wrapper, such thatoperation on the distributed ledger provides provable access to thetrade secret and the wrapper provides validation of the trade secret bythe expert. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aspot market and having a distributed ledger that aggregates views of atrade secret into a chain that proves which and how many parties haveviewed the trade secret. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a distributedledger that tokenizes an instruction set, such that operation on thedistributed ledger provides provable access to the instruction set andexecution of the instruction set on a system results in recording atransaction in the distributed ledger. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a distributedledger that tokenizes an item of intellectual property and a reportingsystem that reports an analytic result based on the operations performedon the distributed ledger or the intellectual property. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a spot market and having adistributed ledger that aggregates a set of instructions, where anoperation on the distributed ledger adds at least one instruction to apre-existing set of instructions to provide a modified set ofinstructions. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aspot market and having a smart wrapper for management of a distributedledger that aggregates sets of instructions, where the smart wrappermanages allocation of instruction sub-sets to the distributed ledger andaccess to the instruction sub-sets. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a smart wrapper fora cryptocurrency coin that directs execution of a transaction involvingthe coin to a geographic location based on tax treatment of at least oneof the coin and the transaction in the geographic location. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a spot market andhaving a self-executing cryptocurrency coin that commits a transactionupon recognizing a location-based parameter that provides favorable taxtreatment. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aspot market and having an expert system that uses machine learning tooptimize the execution of cryptocurrency transactions based on taxstatus. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically purchases energy credits in a spotmarket and having an expert system that aggregates regulatoryinformation covering cryptocurrency transactions and automaticallyselects a jurisdiction for an operation based on the regulatoryinformation. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aspot market and having an expert system that uses machine learning tooptimize the execution of a cryptocurrency transaction based on realtime energy price information for an available energy source. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a spot market andhaving an expert system that uses machine learning to optimize theexecution of a cryptocurrency transaction based on an understanding ofavailable energy sources to power computing resources to execute thetransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aspot market and having an expert system that uses machine learning tooptimize charging and recharging cycle of a rechargeable battery systemto provide energy for execution of a cryptocurrency transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a spot market andhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aspot market and having an expert system that predicts a forward marketprice in a market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aspot market and having an expert system that predicts a forward marketprice in a market based on an understanding obtained by analyzing socialnetwork data sources and executes a cryptocurrency transaction based onthe forward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having an expert systemthat predicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a spot market andhaving an expert system that predicts a forward market price in anenergy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having an expert systemthat predicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aspot market and having an expert system that predicts a forward marketprice in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a spot market andhaving an expert system that predicts a forward market price in a marketfor computing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having an expert systemthat predicts a forward market price in a market for advertising basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aspot market and having an expert system that predicts a forward marketprice in a market for advertising based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a spot market andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases energy credits in a spot marketand having a machine that automatically forecasts forward market valueof compute capability based on information collected from automatedagent behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a spot market andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases energy credits in a spot marketand having a machine that automatically forecasts forward market valueof compute capability based on information collected from businessentity behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a spot market andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aspot market and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a spot market andhaving an expert system that predicts a forward market price in a marketfor spectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having an intelligentagent that is configured to solicit the attention resources of anotherexternal intelligent agent. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a machine thatautomatically purchases attention resources in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aspot market and having a fleet of machines that automatically aggregatepurchasing in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a spot market and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aspot market and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a spot market andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize requisitionand provisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aspot market and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases energy credits in aspot market and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a spot market andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of facility resources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases energy credits in a spot market and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases energy credits in a spot market and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases energy credits in a spot market andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of parametersreceived from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing in a spotmarket for energy. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a fleet of machines that automatically aggregate purchasingenergy credits in a spot market. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a machine that automatically purchases spectrum allocation in aspot market for network spectrum. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a fleet of machines that automatically purchase spectrumallocation in a spot market for network spectrum. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a spot market forenergy and having a fleet of machines that automatically optimize energyutilization for compute task allocation. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a spotmarket for energy and having a fleet of machines that automaticallyaggregate data on collective optimization of spot market purchases ofenergy credits. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of network spectrum. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a spotmarket for energy and having a fleet of machines that automatically selltheir aggregate compute capacity on a forward market for computecapacity. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a spot market for energy and having a fleet of machinesthat automatically sell their aggregate compute storage capacity on aforward market for storage capacity. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a fleet of machines that automatically sell their aggregateenergy storage capacity on a forward market for energy storage capacity.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing in a spotmarket for energy and having a fleet of machines that automatically selltheir aggregate network bandwidth on a forward market for networkcapacity. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a spot market for energy and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a spot market forenergy and having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from socialmedia data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from social media datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a spot market for energy and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a spot market forenergy and having a machine that automatically executes an arbitragestrategy for purchase or sale of compute capacity by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a spot market for energy and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of network spectrum or bandwidth by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a spot market for energy and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of energy credits by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a spotmarket for energy and having a machine that automatically allocates itsenergy capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a spot market forenergy and having a machine that automatically allocates its computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a machine that automatically allocates its networking capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a fleet of machines that automatically allocate collective energycapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a fleet of machines that automatically allocate collectivecompute capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a spot market forenergy and having a fleet of machines that automatically allocatecollective networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a spotmarket for energy and having a smart contract wrapper using adistributed ledger wherein the smart contract embeds IP licensing termsfor intellectual property embedded in the distributed ledger and whereinexecuting an operation on the distributed ledger provides access to theintellectual property and commits the executing party to the IPlicensing terms. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toan aggregate stack of intellectual property. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toagree to an apportionment of royalties among the parties in the ledger.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing in a spotmarket for energy and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a spotmarket for energy and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to commit aparty to a contract term. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a distributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a distributed ledger that tokenizes executable algorithmic logic,such that operation on the distributed ledger provides provable accessto the executable algorithmic logic. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a distributed ledger that tokenizes a 3D printer instruction set,such that operation on the distributed ledger provides provable accessto the instruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a distributed ledger that tokenizes an instruction set for acoating process, such that operation on the distributed ledger providesprovable access to the instruction set. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a distributed ledger that tokenizes an instruction set for asemiconductor fabrication process, such that operation on thedistributed ledger provides provable access to the fabrication process.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing in a spotmarket for energy and having a distributed ledger that tokenizes afirmware program, such that operation on the distributed ledger providesprovable access to the firmware program. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a distributed ledger that tokenizes an instruction set for anFPGA, such that operation on the distributed ledger provides provableaccess to the FPGA. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a distributed ledger that tokenizes serverless code logic, suchthat operation on the distributed ledger provides provable access to theserverless code logic. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a distributed ledger that tokenizes an instruction set for acrystal fabrication system, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a spot market forenergy and having a distributed ledger that tokenizes an instruction setfor a food preparation process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a spot market forenergy and having a distributed ledger that tokenizes an instruction setfor a polymer production process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a spot market forenergy and having a distributed ledger that tokenizes an instruction setfor chemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a spot market forenergy and having a distributed ledger that tokenizes an instruction setfor a biological production process, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a spotmarket for energy and having a distributed ledger that tokenizes a tradesecret with an expert wrapper, such that operation on the distributedledger provides provable access to the trade secret and the wrapperprovides validation of the trade secret by the expert. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a spot market forenergy and having a distributed ledger that aggregates views of a tradesecret into a chain that proves which and how many parties have viewedthe trade secret. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a distributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a spot market forenergy and having a distributed ledger that tokenizes an item ofintellectual property and a reporting system that reports an analyticresult based on the operations performed on the distributed ledger orthe intellectual property. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a distributed ledger that aggregates a set of instructions, wherean operation on the distributed ledger adds at least one instruction toa pre-existing set of instructions to provide a modified set ofinstructions. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a spot market for energy and having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a spotmarket for energy and having a smart wrapper for a cryptocurrency cointhat directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a self-executing cryptocurrency coin that commits a transactionupon recognizing a location-based parameter that provides favorable taxtreatment. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a spot market for energy and having an expert system thatuses machine learning to optimize the execution of cryptocurrencytransactions based on tax status. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving an expert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a spot market forenergy and having an expert system that uses machine learning tooptimize the execution of a cryptocurrency transaction based on realtime energy price information for an available energy source. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a spotmarket for energy and having an expert system that uses machine learningto optimize the execution of a cryptocurrency transaction based on anunderstanding of available energy sources to power computing resourcesto execute the transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving an expert system that uses machine learning to optimize chargingand recharging cycle of a rechargeable battery system to provide energyfor execution of a cryptocurrency transaction. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a spot market for energy and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a spot market forenergy and having an expert system that predicts a forward market pricein a market based on an understanding obtained by analyzing Internet ofThings data sources and executes a cryptocurrency transaction based onthe forward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing social network datasources and executes a cryptocurrency transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving an expert system that predicts a forward market price in anenergy market based on an understanding obtained by analyzing Internetof Things data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving an expert system that predicts a forward market price in anenergy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving an expert system that predicts a forward market price in a marketfor computing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving an expert system that predicts a forward market price in a marketfor spectrum or network bandwidth based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving an expert system that predicts a forward market price in a marketfor computing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving an expert system that predicts a forward market price in a marketfor advertising based on an understanding obtained by analyzing Internetof Things data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving an expert system that predicts a forward market price in a marketfor advertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a spot market for energy and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a spotmarket for energy and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom human behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a spot market for energy and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing social datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a spot market for energy and having an intelligent agentthat is configured to solicit the attention resources of anotherexternal intelligent agent. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a machine that automatically purchases attention resources in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a fleet of machines that automatically aggregate purchasing in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a likelihoodof a facility production outcome. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a facilityproduction outcome. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing in a spotmarket for energy and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize requisitionand provisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing in a spot market for energy and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize selectionand configuration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a spot market forenergy and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to at least one of an input resource, afacility resource, an output parameter and an external condition relatedto the output of the facility. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of input resources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing in a spot market forenergy and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a spotmarket for energy and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to an output parameter. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing in a spotmarket for energy and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing in a spot market for energy andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of parametersreceived from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing energycredits in a spot market. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a machine that automatically purchases spectrum allocation in aspot market for network spectrum. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a fleet of machines that automatically purchase spectrumallocation in a spot market for network spectrum. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aspot market and having a fleet of machines that automatically optimizeenergy utilization for compute task allocation. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a spot market and having a fleet of machines that automaticallyaggregate data on collective optimization of spot market purchases ofenergy credits. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of network spectrum. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a spot market and having a fleet of machines that automatically selltheir aggregate compute capacity on a forward market for computecapacity. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a spot market and having a fleet ofmachines that automatically sell their aggregate compute storagecapacity on a forward market for storage capacity. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aspot market and having a fleet of machines that automatically sell theiraggregate energy storage capacity on a forward market for energy storagecapacity. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a spot market and having a fleet ofmachines that automatically sell their aggregate network bandwidth on aforward market for network capacity. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from social media datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a spot market and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aspot market and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromsocial media data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from social media datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a spot market and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of energy storage capacity by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a spot market and having a machine that automatically executes anarbitrage strategy for purchase or sale of network spectrum or bandwidthby testing a spot market for compute capacity with a small transactionand rapidly executing a larger transaction based on the outcome of thesmall transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of energy by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of energy credits by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a spot market and having a machine that automatically allocates itsenergy capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aspot market and having a machine that automatically allocates itscompute capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aspot market and having a machine that automatically allocates itsnetworking capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aspot market and having a fleet of machines that automatically allocatecollective energy capacity among a core task, a compute task, an energystorage task, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aspot market and having a fleet of machines that automatically allocatecollective compute capacity among a core task, a compute task, an energystorage task, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aspot market and having a fleet of machines that automatically allocatecollective networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a spot market and having a smart contract wrapper using a distributedledger wherein the smart contract embeds IP licensing terms forintellectual property embedded in the distributed ledger and whereinexecuting an operation on the distributed ledger provides access to theintellectual property and commits the executing party to the IPlicensing terms. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toan aggregate stack of intellectual property. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toagree to an apportionment of royalties among the parties in the ledger.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing energycredits in a spot market and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a spot market and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to commit aparty to a contract term. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a distributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a distributed ledger that tokenizes executable algorithmic logic,such that operation on the distributed ledger provides provable accessto the executable algorithmic logic. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a distributed ledger that tokenizes a 3D printer instruction set,such that operation on the distributed ledger provides provable accessto the instruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a distributed ledger that tokenizes an instruction set for acoating process, such that operation on the distributed ledger providesprovable access to the instruction set. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a distributed ledger that tokenizes an instruction set for asemiconductor fabrication process, such that operation on thedistributed ledger provides provable access to the fabrication process.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing energycredits in a spot market and having a distributed ledger that tokenizesa firmware program, such that operation on the distributed ledgerprovides provable access to the firmware program. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aspot market and having a distributed ledger that tokenizes aninstruction set for an FPGA, such that operation on the distributedledger provides provable access to the FPGA. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a distributed ledger that tokenizes serverless code logic, suchthat operation on the distributed ledger provides provable access to theserverless code logic. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a distributed ledger that tokenizes an instruction set for acrystal fabrication system, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aspot market and having a distributed ledger that tokenizes aninstruction set for a food preparation process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing energycredits in a spot market and having a distributed ledger that tokenizesan instruction set for a polymer production process, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically aggregate purchasingenergy credits in a spot market and having a distributed ledger thattokenizes an instruction set for chemical synthesis process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a distributed ledger that tokenizes an instruction set for abiological production process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aspot market and having a distributed ledger that tokenizes a tradesecret with an expert wrapper, such that operation on the distributedledger provides provable access to the trade secret and the wrapperprovides validation of the trade secret by the expert. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aspot market and having a distributed ledger that aggregates views of atrade secret into a chain that proves which and how many parties haveviewed the trade secret. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a distributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aspot market and having a distributed ledger that tokenizes an item ofintellectual property and a reporting system that reports an analyticresult based on the operations performed on the distributed ledger orthe intellectual property. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a distributed ledger that aggregates a set of instructions, wherean operation on the distributed ledger adds at least one instruction toa pre-existing set of instructions to provide a modified set ofinstructions. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a spot market and having a smart wrapperfor management of a distributed ledger that aggregates sets ofinstructions, where the smart wrapper manages allocation of instructionsub-sets to the distributed ledger and access to the instructionsub-sets. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a spot market and having a smart wrapperfor a cryptocurrency coin that directs execution of a transactioninvolving the coin to a geographic location based on tax treatment of atleast one of the coin and the transaction in the geographic location. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a spot market and having a self-executing cryptocurrency coin thatcommits a transaction upon recognizing a location-based parameter thatprovides favorable tax treatment. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving an expert system that uses machine learning to optimize theexecution of cryptocurrency transactions based on tax status. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a spot market and having an expert system that aggregates regulatoryinformation covering cryptocurrency transactions and automaticallyselects a jurisdiction for an operation based on the regulatoryinformation. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a spot market and having an expert systemthat uses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a spot market and having an expert systemthat uses machine learning to optimize the execution of a cryptocurrencytransaction based on an understanding of available energy sources topower computing resources to execute the transaction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aspot market and having an expert system that uses machine learning tooptimize charging and recharging cycle of a rechargeable battery systemto provide energy for execution of a cryptocurrency transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a spot market and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a spot market and having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a spot market and having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a spot market and having an expert systemthat predicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a spot market and having an expert system that predicts a forwardmarket price in an energy market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving an expert system that predicts a forward market price in a marketfor computing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving an expert system that predicts a forward market price in a marketfor spectrum or network bandwidth based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving an expert system that predicts a forward market price in a marketfor computing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving an expert system that predicts a forward market price in a marketfor advertising based on an understanding obtained by analyzing Internetof Things data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving an expert system that predicts a forward market price in a marketfor advertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a spot market and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a spot market and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom human behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a spot market and having an expert systemthat predicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing social datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a spot market and having an intelligentagent that is configured to solicit the attention resources of anotherexternal intelligent agent. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a machine that automatically purchases attention resources in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a fleet of machines that automatically aggregate purchasing in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a likelihoodof a facility production outcome. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a facilityproduction outcome. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate purchasing energycredits in a spot market and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize provisioning and allocation of energy and compute resourcesto produce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize requisitionand provisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregatepurchasing energy credits in a spot market and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a spot market and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a spot market and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a spot market and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate purchasing energy credits in aspot market and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a spot market and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a spot market and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to an output parameter. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate purchasing energy creditsin a spot market and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate purchasing energy credits in a spot market andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of parametersreceived from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases spectrum allocation in a spotmarket for network spectrum. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a fleet of machines that automatically purchase spectrumallocation in a spot market for network spectrum. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum and having a fleet of machines that automatically optimizeenergy utilization for compute task allocation. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum and having a fleet of machines that automatically aggregatedata on collective optimization of spot market purchases of energy. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a spotmarket for network spectrum and having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of network spectrum. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a spotmarket for network spectrum and having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a fleet of machines that automatically sell their aggregatecompute storage capacity on a forward market for storage capacity. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a spotmarket for network spectrum and having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a fleet of machines that automatically sell their aggregatenetwork bandwidth on a forward market for network capacity. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a spotmarket for network spectrum and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected fromsocial media data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from social media datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases spectrum allocationin a spot market for network spectrum and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum and having a machine that automatically executes an arbitragestrategy for purchase or sale of compute capacity by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases spectrum allocationin a spot market for network spectrum and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum and having a machine that automatically executes an arbitragestrategy for purchase or sale of network spectrum or bandwidth bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases spectrum allocationin a spot market for network spectrum and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of energy credits by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a spotmarket for network spectrum and having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a spotmarket for network spectrum and having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a spotmarket for network spectrum and having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a spotmarket for network spectrum and having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a fleet of machines that automatically allocate collectivecompute capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum and having a fleet of machines that automatically allocatecollective networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a spotmarket for network spectrum and having a smart contract wrapper using adistributed ledger wherein the smart contract embeds IP licensing termsfor intellectual property embedded in the distributed ledger and whereinexecuting an operation on the distributed ledger provides access to theintellectual property and commits the executing party to the IPlicensing terms. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toan aggregate stack of intellectual property. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum and having a distributed ledger for aggregating intellectualproperty licensing terms, wherein a smart contract wrapper on thedistributed ledger allows an operation on the ledger to add intellectualproperty to agree to an apportionment of royalties among the parties inthe ledger. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases spectrum allocationin a spot market for network spectrum and having a distributed ledgerfor aggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to commit a party to a contractterm. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically purchases spectrum allocation in aspot market for network spectrum and having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum and having a distributed ledger that tokenizes executablealgorithmic logic, such that operation on the distributed ledgerprovides provable access to the executable algorithmic logic. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a spotmarket for network spectrum and having a distributed ledger thattokenizes a 3D printer instruction set, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a spotmarket for network spectrum and having a distributed ledger thattokenizes an instruction set for a coating process, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically purchases spectrum allocation in aspot market for network spectrum and having a distributed ledger thattokenizes an instruction set for a semiconductor fabrication process,such that operation on the distributed ledger provides provable accessto the fabrication process. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a distributed ledger that tokenizes a firmware program, such thatoperation on the distributed ledger provides provable access to thefirmware program. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a distributed ledger that tokenizes an instruction set for anFPGA, such that operation on the distributed ledger provides provableaccess to the FPGA. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a distributed ledger that tokenizes serverless code logic, suchthat operation on the distributed ledger provides provable access to theserverless code logic. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a distributed ledger that tokenizes an instruction set for acrystal fabrication system, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum and having a distributed ledger that tokenizes an instructionset for a food preparation process, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a spotmarket for network spectrum and having a distributed ledger thattokenizes an instruction set for a polymer production process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a distributed ledger that tokenizes an instruction set forchemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum and having a distributed ledger that tokenizes an instructionset for a biological production process, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a spotmarket for network spectrum and having a distributed ledger thattokenizes a trade secret with an expert wrapper, such that operation onthe distributed ledger provides provable access to the trade secret andthe wrapper provides validation of the trade secret by the expert. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a spotmarket for network spectrum and having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum and having a distributed ledger that tokenizes an instructionset, such that operation on the distributed ledger provides provableaccess to the instruction set and execution of the instruction set on asystem results in recording a transaction in the distributed ledger. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a spotmarket for network spectrum and having a distributed ledger thattokenizes an item of intellectual property and a reporting system thatreports an analytic result based on the operations performed on thedistributed ledger or the intellectual property. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum and having a distributed ledger that aggregates a set ofinstructions, where an operation on the distributed ledger adds at leastone instruction to a pre-existing set of instructions to provide amodified set of instructions. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a smart wrapper for management of a distributed ledger thataggregates sets of instructions, where the smart wrapper managesallocation of instruction sub-sets to the distributed ledger and accessto the instruction sub-sets. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a smart wrapper for a cryptocurrency coin that directs executionof a transaction involving the coin to a geographic location based ontax treatment of at least one of the coin and the transaction in thegeographic location. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a self-executing cryptocurrency coin that commits a transactionupon recognizing a location-based parameter that provides favorable taxtreatment. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases spectrum allocationin a spot market for network spectrum and having an expert system thatuses machine learning to optimize the execution of cryptocurrencytransactions based on tax status. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving an expert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum and having an expert system that uses machine learning tooptimize the execution of a cryptocurrency transaction based on realtime energy price information for an available energy source. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a spotmarket for network spectrum and having an expert system that usesmachine learning to optimize the execution of a cryptocurrencytransaction based on an understanding of available energy sources topower computing resources to execute the transaction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum and having an expert system that uses machine learning tooptimize charging and recharging cycle of a rechargeable battery systemto provide energy for execution of a cryptocurrency transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a spotmarket for network spectrum and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases spectrum allocationin a spot market for network spectrum and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a spotmarket for network spectrum and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum and having an expert system that predicts a forward marketprice in an energy market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving an expert system that predicts a forward market price in anenergy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving an expert system that predicts a forward market price in a marketfor computing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving an expert system that predicts a forward market price in a marketfor spectrum or network bandwidth based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving an expert system that predicts a forward market price in a marketfor computing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving an expert system that predicts a forward market price in a marketfor advertising based on an understanding obtained by analyzing Internetof Things data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving an expert system that predicts a forward market price in a marketfor advertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically purchases spectrum allocationin a spot market for network spectrum and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a spotmarket for network spectrum and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving an expert system that predicts a forward market price in a marketfor spectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving an intelligent agent that is configured to solicit the attentionresources of another external intelligent agent. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum and having a machine that automatically purchases attentionresources in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum and having a fleet of machines that automatically aggregatepurchasing in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to predict alikelihood of a facility production outcome. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to predict afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases spectrum allocation in a spotmarket for network spectrum and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource outputselection among a set of available outputs. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizerequisition and provisioning of available energy and compute resourcesto produce a favorable facility input resource profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimizeconfiguration of available energy and compute resources to produce afavorable facility resource configuration profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize selectionand configuration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to at least one of an input resource, afacility resource, an output parameter and an external condition relatedto the output of the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallypurchases spectrum allocation in a spot market for network spectrum andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of input resources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically purchases spectrum allocation in a spotmarket for network spectrum and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to an output parameter.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically purchases spectrum allocation in a spotmarket for network spectrum and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a utilizationparameter for the output of the facility. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically purchases spectrum allocation in a spot market for networkspectrum and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofparameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a fleet of machines that automatically optimizeenergy utilization for compute task allocation. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a fleet of machines that automatically aggregatedata on collective optimization of spot market purchases of energy. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum and having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a fleet of machines that automatically aggregatedata on collective optimization of spot market purchases of networkspectrum. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically purchase spectrumallocation in a spot market for network spectrum and having a fleet ofmachines that automatically sell their aggregate compute capacity on aforward market for compute capacity. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a fleet of machines that automatically sell theiraggregate compute storage capacity on a forward market for storagecapacity. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically purchase spectrumallocation in a spot market for network spectrum and having a fleet ofmachines that automatically sell their aggregate energy storage capacityon a forward market for energy storage capacity. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a spotmarket for network spectrum and having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a machine that automatically forecasts forwardmarket pricing of energy prices based on information collected fromsocial media data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected fromsocial media data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromsocial media data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromsocial media data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a machine that automatically executes an arbitragestrategy for purchase or sale of compute capacity by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically purchase spectrumallocation in a spot market for network spectrum and having a machinethat automatically executes an arbitrage strategy for purchase or saleof energy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a machine that automatically executes an arbitragestrategy for purchase or sale of network spectrum or bandwidth bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically purchase spectrumallocation in a spot market for network spectrum and having a machinethat automatically executes an arbitrage strategy for purchase or saleof energy by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a machine that automatically executes an arbitragestrategy for purchase or sale of energy credits by testing a spot marketfor compute capacity with a small transaction and rapidly executing alarger transaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum and having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum and having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum and having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum and having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a fleet of machines that automatically allocatecollective compute capacity among a core task, a compute task, an energystorage task, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a spotmarket for network spectrum and having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a smart contract wrapper using a distributed ledgerwherein the smart contract embeds IP licensing terms for intellectualproperty embedded in the distributed ledger and wherein executing anoperation on the distributed ledger provides access to the intellectualproperty and commits the executing party to the IP licensing terms. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a distributed ledger for aggregating intellectualproperty licensing terms, wherein a smart contract wrapper on thedistributed ledger allows an operation on the ledger to add intellectualproperty to agree to an apportionment of royalties among the parties inthe ledger. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically purchase spectrumallocation in a spot market for network spectrum and having adistributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to an aggregatestack of intellectual property. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a distributed ledger for aggregating intellectualproperty licensing terms, wherein a smart contract wrapper on thedistributed ledger allows an operation on the ledger to commit a partyto a contract term. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a distributed ledger that tokenizes an instructionset, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a distributed ledger that tokenizes executablealgorithmic logic, such that operation on the distributed ledgerprovides provable access to the executable algorithmic logic. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum and having a distributed ledger thattokenizes a 3D printer instruction set, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum and having a distributed ledger thattokenizes an instruction set for a coating process, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically purchase spectrumallocation in a spot market for network spectrum and having adistributed ledger that tokenizes an instruction set for a semiconductorfabrication process, such that operation on the distributed ledgerprovides provable access to the fabrication process. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a spotmarket for network spectrum and having a distributed ledger thattokenizes a firmware program, such that operation on the distributedledger provides provable access to the firmware program. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a spotmarket for network spectrum and having a distributed ledger thattokenizes an instruction set for an FPGA, such that operation on thedistributed ledger provides provable access to the FPGA. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a spotmarket for network spectrum and having a distributed ledger thattokenizes serverless code logic, such that operation on the distributedledger provides provable access to the serverless code logic. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum and having a distributed ledger thattokenizes an instruction set for a crystal fabrication system, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a distributed ledger that tokenizes an instructionset for a food preparation process, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum and having a distributed ledger thattokenizes an instruction set for a polymer production process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a distributed ledger that tokenizes an instructionset for chemical synthesis process, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum and having a distributed ledger thattokenizes an instruction set for a biological production process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a distributed ledger that tokenizes a trade secretwith an expert wrapper, such that operation on the distributed ledgerprovides provable access to the trade secret and the wrapper providesvalidation of the trade secret by the expert. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a distributed ledger that aggregates views of atrade secret into a chain that proves which and how many parties haveviewed the trade secret. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a distributed ledger that tokenizes an instructionset, such that operation on the distributed ledger provides provableaccess to the instruction set and execution of the instruction set on asystem results in recording a transaction in the distributed ledger. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum and having a distributed ledger thattokenizes an item of intellectual property and a reporting system thatreports an analytic result based on the operations performed on thedistributed ledger or the intellectual property. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a spotmarket for network spectrum and having a distributed ledger thataggregates a set of instructions, where an operation on the distributedledger adds at least one instruction to a pre-existing set ofinstructions to provide a modified set of instructions. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a spotmarket for network spectrum and having a smart wrapper for management ofa distributed ledger that aggregates sets of instructions, where thesmart wrapper manages allocation of instruction sub-sets to thedistributed ledger and access to the instruction sub-sets. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum and having a smart wrapper for acryptocurrency coin that directs execution of a transaction involvingthe coin to a geographic location based on tax treatment of at least oneof the coin and the transaction in the geographic location. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum and having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum and having an expert system that usesmachine learning to optimize the execution of cryptocurrencytransactions based on tax status. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having an expert system that aggregates regulatoryinformation covering cryptocurrency transactions and automaticallyselects a jurisdiction for an operation based on the regulatoryinformation. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically purchase spectrumallocation in a spot market for network spectrum and having an expertsystem that uses machine learning to optimize the execution of acryptocurrency transaction based on real time energy price informationfor an available energy source. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having an expert system that uses machine learning tooptimize the execution of a cryptocurrency transaction based on anunderstanding of available energy sources to power computing resourcesto execute the transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having an expert system that uses machine learning tooptimize charging and recharging cycle of a rechargeable battery systemto provide energy for execution of a cryptocurrency transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a spotmarket for network spectrum and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having an expert system that predicts a forward marketprice in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a spotmarket for network spectrum and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a spotmarket for network spectrum and having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a spotmarket for network spectrum and having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a spotmarket for network spectrum and having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having an expert system that predicts a forward marketprice in a market for computing resources based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a spotmarket for network spectrum and having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum and having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a spotmarket for network spectrum and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a spotmarket for network spectrum and having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a spotmarket for network spectrum and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a spotmarket for network spectrum and having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having an expert system that predicts a forward marketprice in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing social data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a spotmarket for network spectrum and having an intelligent agent that isconfigured to solicit the attention resources of another externalintelligent agent. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a machine that automatically purchases attentionresources in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a fleet of machines that automatically aggregatepurchasing in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to predict alikelihood of a facility production outcome. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to predict afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizeprovisioning and allocation of energy and compute resources to produce afavorable facility resource utilization profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically purchase spectrumallocation in a spot market for network spectrum and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize requisition and provisioning of availableenergy and compute resources to produce a favorable facility inputresource profile among a set of available profiles. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a spotmarket for network spectrum and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to optimizeselection and configuration of an artificial intelligence system toproduce a favorable facility output profile among a set of availableartificial intelligence systems and configurations. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically purchase spectrum allocation in a spotmarket for network spectrum and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to generate an indication that a current or prospectivecustomer should be contacted about an output that can be provided by thefacility. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically purchase spectrumallocation in a spot market for network spectrum and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically purchase spectrumallocation in a spot market for network spectrum and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of input resources. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically purchase spectrum allocation in aspot market for network spectrum and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to anoutput parameter. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a utilization parameter for the outputof the facility. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically purchase spectrum allocation in a spot market for networkspectrum and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofparameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically optimize energy utilization forcompute task allocation. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of network spectrum. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a fleet of machines that automatically sell their aggregatecompute storage capacity on a forward market for storage capacity. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a fleet of machines that automatically sell their aggregatenetwork bandwidth on a forward market for network capacity. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically forecasts forward market pricingof network spectrum based on information collected from social mediadata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically optimize energyutilization for compute task allocation and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically optimize energy utilization for compute taskallocation and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromsocial media data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically executes an arbitrage strategyfor purchase or sale of compute capacity by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a machine that automatically executesan arbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically optimize energyutilization for compute task allocation and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a machine that automatically executesan arbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically optimize energyutilization for compute task allocation and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy credits by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically allocates its energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically allocates its compute capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically allocates its networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a fleet of machines that automatically allocate collectiveenergy capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically optimize energy utilization for compute taskallocation and having a fleet of machines that automatically allocatecollective compute capacity among a core task, a compute task, an energystorage task, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically optimize energy utilization for compute taskallocation and having a fleet of machines that automatically allocatecollective networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a smart contract wrapper using adistributed ledger wherein the smart contract embeds IP licensing termsfor intellectual property embedded in the distributed ledger and whereinexecuting an operation on the distributed ledger provides access to theintellectual property and commits the executing party to the IPlicensing terms. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toan aggregate stack of intellectual property. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toagree to an apportionment of royalties among the parties in the ledger.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically optimize energy utilization forcompute task allocation and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to commit aparty to a contract term. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger that tokenizes an instruction set, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger that tokenizes executable algorithmiclogic, such that operation on the distributed ledger provides provableaccess to the executable algorithmic logic. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger that tokenizes a 3D printer instructionset, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger that tokenizes an instruction set for acoating process, such that operation on the distributed ledger providesprovable access to the instruction set. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger that tokenizes an instruction set for asemiconductor fabrication process, such that operation on thedistributed ledger provides provable access to the fabrication process.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically optimize energy utilization forcompute task allocation and having a distributed ledger that tokenizes afirmware program, such that operation on the distributed ledger providesprovable access to the firmware program. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger that tokenizes an instruction set for anFPGA, such that operation on the distributed ledger provides provableaccess to the FPGA. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger that tokenizes serverless code logic,such that operation on the distributed ledger provides provable accessto the serverless code logic. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger that tokenizes an instruction set for acrystal fabrication system, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically optimize energy utilization for compute taskallocation and having a distributed ledger that tokenizes an instructionset for a food preparation process, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a distributed ledger that tokenizesan instruction set for a polymer production process, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically optimize energyutilization for compute task allocation and having a distributed ledgerthat tokenizes an instruction set for chemical synthesis process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger that tokenizes an instruction set for abiological production process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically optimize energy utilization for compute taskallocation and having a distributed ledger that tokenizes a trade secretwith an expert wrapper, such that operation on the distributed ledgerprovides provable access to the trade secret and the wrapper providesvalidation of the trade secret by the expert. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger that aggregates views of a trade secretinto a chain that proves which and how many parties have viewed thetrade secret. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically optimize energyutilization for compute task allocation and having a distributed ledgerthat tokenizes an instruction set, such that operation on thedistributed ledger provides provable access to the instruction set andexecution of the instruction set on a system results in recording atransaction in the distributed ledger. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a distributed ledger that tokenizes an item of intellectualproperty and a reporting system that reports an analytic result based onthe operations performed on the distributed ledger or the intellectualproperty. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically optimize energyutilization for compute task allocation and having a distributed ledgerthat aggregates a set of instructions, where an operation on thedistributed ledger adds at least one instruction to a pre-existing setof instructions to provide a modified set of instructions. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a smart wrapper for management of adistributed ledger that aggregates sets of instructions, where the smartwrapper manages allocation of instruction sub-sets to the distributedledger and access to the instruction sub-sets. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a self-executing cryptocurrency coin that commits atransaction upon recognizing a location-based parameter that providesfavorable tax treatment. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having an expert system that uses machine learning to optimize theexecution of cryptocurrency transactions based on tax status. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having an expert system that uses machine learning to optimize theexecution of a cryptocurrency transaction based on real time energyprice information for an available energy source. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically optimize energy utilization for compute taskallocation and having an expert system that uses machine learning tooptimize the execution of a cryptocurrency transaction based on anunderstanding of available energy sources to power computing resourcesto execute the transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically optimize energyutilization for compute task allocation and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically optimize energy utilization for compute taskallocation and having an expert system that predicts a forward marketprice in an energy market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having an expert system that predicts a forward market price in anenergy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having an expert system that predicts a forward market price in amarket for computing resources based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically optimize energy utilization for compute taskallocation and having an expert system that predicts a forward marketprice in a market for computing resources based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically optimize energy utilization for compute taskallocation and having an expert system that predicts a forward marketprice in a market for advertising based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having an expert system that predicts a forward market price in amarket for advertising based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically forecasts forward market pricingof energy prices based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically forecasts forward market pricingof network spectrum based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically forecasts forward market pricingof energy credits based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically forecasts forward market valueof compute capability based on information collected from automatedagent behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically forecasts forward market pricingof energy prices based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically forecasts forward market pricingof network spectrum based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically forecasts forward market pricingof energy credits based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically forecasts forward market valueof compute capability based on information collected from businessentity behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically forecasts forward market pricingof energy prices based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically optimize energyutilization for compute task allocation and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a machine that automatically forecasts forward market valueof compute capability based on information collected from humanbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing social data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having an intelligent agent that is configured to solicit theattention resources of another external intelligent agent. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a machine that automaticallypurchases attention resources in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto predict a likelihood of a facility production outcome. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto predict a facility production outcome. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically optimize energy utilization forcompute task allocation and having a system for learning on a trainingset of facility outcomes, facility parameters, and data collected fromdata sources to train an artificial intelligence/machine learning systemto optimize provisioning and allocation of energy and compute resourcesto produce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize requisitionand provisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically optimize energyutilization for compute task allocation and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize selectionand configuration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically optimize energy utilization for compute taskallocation and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically optimize energy utilization for compute taskallocation and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically optimize energy utilization forcompute task allocation and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically optimize energyutilization for compute task allocation and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofdetected conditions relating to a utilization parameter for the outputof the facility. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically optimize energy utilization for compute task allocationand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofparameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy and having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a fleet of machines that automaticallyaggregate data on collective optimization of spot market purchases ofnetwork spectrum. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a fleet of machines that automaticallysell their aggregate compute capacity on a forward market for computecapacity. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy and having afleet of machines that automatically sell their aggregate computestorage capacity on a forward market for storage capacity. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy and having a fleet ofmachines that automatically sell their aggregate energy storage capacityon a forward market for energy storage capacity. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy and having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a machine that automatically forecastsforward market pricing of energy prices based on information collectedfrom social media data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from social media data sources. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom social media data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from social media data sources. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a machine that automatically executes anarbitrage strategy for purchase or sale of compute capacity by testing aspot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy and having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy and having a machinethat automatically executes an arbitrage strategy for purchase or saleof network spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy and having a machinethat automatically executes an arbitrage strategy for purchase or saleof energy by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a machine that automatically executes anarbitrage strategy for purchase or sale of energy credits by testing aspot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy and having amachine that automatically allocates its energy capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a machine that automatically allocatesits compute capacity among a core task, a compute task, an energystorage task, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy and having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy and having a fleet ofmachines that automatically allocate collective energy capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a fleet of machines that automaticallyallocate collective compute capacity among a core task, a compute task,an energy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy and having a fleet ofmachines that automatically allocate collective networking capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a smart contract wrapper using adistributed ledger wherein the smart contract embeds IP licensing termsfor intellectual property embedded in the distributed ledger and whereinexecuting an operation on the distributed ledger provides access to theintellectual property and commits the executing party to the IPlicensing terms. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy and having a distributedledger for aggregating intellectual property licensing terms, wherein asmart contract wrapper on the distributed ledger allows an operation onthe ledger to add intellectual property to agree to an apportionment ofroyalties among the parties in the ledger. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy and having a distributedledger for aggregating intellectual property licensing terms, wherein asmart contract wrapper on the distributed ledger allows an operation onthe ledger to commit a party to a contract term. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy and having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy and having a distributed ledger thattokenizes executable algorithmic logic, such that operation on thedistributed ledger provides provable access to the executablealgorithmic logic. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a distributed ledger that tokenizes a 3Dprinter instruction set, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy and having a distributed ledger thattokenizes an instruction set for a coating process, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy and having adistributed ledger that tokenizes an instruction set for a semiconductorfabrication process, such that operation on the distributed ledgerprovides provable access to the fabrication process. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy and having a distributed ledger thattokenizes a firmware program, such that operation on the distributedledger provides provable access to the firmware program. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy and having a distributed ledger thattokenizes an instruction set for an FPGA, such that operation on thedistributed ledger provides provable access to the FPGA. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy and having a distributed ledger thattokenizes serverless code logic, such that operation on the distributedledger provides provable access to the serverless code logic. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy and having a distributedledger that tokenizes an instruction set for a crystal fabricationsystem, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a distributed ledger that tokenizes aninstruction set for a food preparation process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy and having a distributedledger that tokenizes an instruction set for a polymer productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a distributed ledger that tokenizes aninstruction set for chemical synthesis process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy and having a distributedledger that tokenizes an instruction set for a biological productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a distributed ledger that tokenizes atrade secret with an expert wrapper, such that operation on thedistributed ledger provides provable access to the trade secret and thewrapper provides validation of the trade secret by the expert. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy and having a distributedledger that aggregates views of a trade secret into a chain that proveswhich and how many parties have viewed the trade secret. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy and having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set and execution ofthe instruction set on a system results in recording a transaction inthe distributed ledger. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a distributed ledger that tokenizes anitem of intellectual property and a reporting system that reports ananalytic result based on the operations performed on the distributedledger or the intellectual property. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a distributed ledger that aggregates aset of instructions, where an operation on the distributed ledger addsat least one instruction to a pre-existing set of instructions toprovide a modified set of instructions. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a smart wrapper for management of adistributed ledger that aggregates sets of instructions, where the smartwrapper manages allocation of instruction sub-sets to the distributedledger and access to the instruction sub-sets. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a smart wrapper for a cryptocurrency cointhat directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a self-executing cryptocurrency coin thatcommits a transaction upon recognizing a location-based parameter thatprovides favorable tax treatment. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy and having anexpert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy and having an expert system that usesmachine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy and having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy and having an expertsystem that uses machine learning to optimize charging and rechargingcycle of a rechargeable battery system to provide energy for executionof a cryptocurrency transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing social network data sources and executes acryptocurrency transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy and having an expertsystem that predicts a forward market price in an energy market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy and having an expertsystem that predicts a forward market price in an energy market based onan understanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy and having an expertsystem that predicts a forward market price in a market for computingresources based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having an expert system that predicts a forwardmarket price in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy and having an expertsystem that predicts a forward market price in a market for computingresources based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy and having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having an expert system that predicts a forwardmarket price in a market for advertising based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy and having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from automated agent behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy and having amachine that automatically forecasts forward market value of computecapability based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a machine that automatically forecastsforward market pricing of energy prices based on information collectedfrom business entity behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy and having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy and having a machinethat automatically forecasts forward market pricing of energy pricesbased on information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy and having a machinethat automatically forecasts forward market pricing of network spectrumbased on information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy and having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy and having a machinethat automatically forecasts forward market value of compute capabilitybased on information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy and having an expertsystem that predicts a forward market price in a market for spectrum ornetwork bandwidth based on an understanding obtained by analyzing socialdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy and having anintelligent agent that is configured to solicit the attention resourcesof another external intelligent agent. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a machine that automatically purchasesattention resources in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy and having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a facility production outcome. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to at least one of aninput resource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to a setof input resources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a utilizationparameter for the output of the facility. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of parameters received from a digital twin for thefacility.

In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy credits. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy credits and having a fleet of machinesthat automatically aggregate data on collective optimization of spotmarket purchases of network spectrum. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy credits and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy credits and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy credits and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy storagecapacity by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of network spectrumor bandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy credits andhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of energy credits by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy credits and having amachine that automatically allocates its energy capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy credits and having amachine that automatically allocates its networking capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a smart contract wrapper using adistributed ledger wherein the smart contract embeds IP licensing termsfor intellectual property embedded in the distributed ledger and whereinexecuting an operation on the distributed ledger provides access to theintellectual property and commits the executing party to the IPlicensing terms. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to agree to an apportionment ofroyalties among the parties in the ledger. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to commit a party to a contract term. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy credits and having a distributed ledgerthat tokenizes executable algorithmic logic, such that operation on thedistributed ledger provides provable access to the executablealgorithmic logic. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a distributed ledger thattokenizes a 3D printer instruction set, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy credits and having adistributed ledger that tokenizes an instruction set for a coatingprocess, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a distributed ledger thattokenizes an instruction set for a semiconductor fabrication process,such that operation on the distributed ledger provides provable accessto the fabrication process. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a distributed ledger thattokenizes a firmware program, such that operation on the distributedledger provides provable access to the firmware program. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy credits and having a distributed ledgerthat tokenizes an instruction set for an FPGA, such that operation onthe distributed ledger provides provable access to the FPGA. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy credits and having adistributed ledger that tokenizes serverless code logic, such thatoperation on the distributed ledger provides provable access to theserverless code logic. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a distributed ledger thattokenizes an instruction set for a crystal fabrication system, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a distributed ledger thattokenizes an instruction set for a food preparation process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a distributed ledger thattokenizes an instruction set for a polymer production process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a distributed ledger thattokenizes an instruction set for chemical synthesis process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a distributed ledger thattokenizes an instruction set for a biological production process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a distributed ledger thattokenizes a trade secret with an expert wrapper, such that operation onthe distributed ledger provides provable access to the trade secret andthe wrapper provides validation of the trade secret by the expert. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy credits and having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy credits and having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy credits and having a distributed ledgerthat tokenizes an item of intellectual property and a reporting systemthat reports an analytic result based on the operations performed on thedistributed ledger or the intellectual property. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy credits and having a distributed ledgerthat aggregates a set of instructions, where an operation on thedistributed ledger adds at least one instruction to a pre-existing setof instructions to provide a modified set of instructions. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy credits and having asmart wrapper for management of a distributed ledger that aggregatessets of instructions, where the smart wrapper manages allocation ofinstruction sub-sets to the distributed ledger and access to theinstruction sub-sets. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a smart wrapper for acryptocurrency coin that directs execution of a transaction involvingthe coin to a geographic location based on tax treatment of at least oneof the coin and the transaction in the geographic location. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy credits and having aself-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy credits andhaving an expert system that uses machine learning to optimize theexecution of cryptocurrency transactions based on tax status. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy credits and having anexpert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy credits and having an expert system thatuses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy credits andhaving an expert system that uses machine learning to optimize theexecution of a cryptocurrency transaction based on an understanding ofavailable energy sources to power computing resources to execute thetransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy credits andhaving an expert system that uses machine learning to optimize chargingand recharging cycle of a rechargeable battery system to provide energyfor execution of a cryptocurrency transaction. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy credits and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing social network data sources and executes acryptocurrency transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy credits and having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy credits andhaving an expert system that predicts a forward market price in anenergy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy credits and having anexpert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy credits and having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having an expert system that predicts aforward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy credits and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy credits andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy credits and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy credits and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy credits andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy credits and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy credits and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy credits andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy credits andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy credits andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy credits andhaving an expert system that predicts a forward market price in a marketfor spectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having an intelligent agent that isconfigured to solicit the attention resources of another externalintelligent agent. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a machine that automaticallypurchases attention resources in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy credits and having afleet of machines that automatically aggregate purchasing in a forwardmarket for attention. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a likelihood of a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy credits andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a facilityproduction outcome. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource utilizationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource outputselection among a set of available outputs. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize requisition and provisioning of availableenergy and compute resources to produce a favorable facility inputresource profile among a set of available profiles. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy credits and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize selection and configuration of an artificialintelligence system to produce a favorable facility output profile amonga set of available artificial intelligence systems and configurations.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of energy credits and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to at least one of aninput resource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of energy credits and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of input resources. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of energy credits andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a utilizationparameter for the output of the facility. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of energy credits and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of parameters received from a digital twin for thefacility.

In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of network spectrum. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of network spectrum and having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of network spectrum and having a fleet of machinesthat automatically sell their aggregate compute storage capacity on aforward market for storage capacity. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of network spectrum and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of network spectrum and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of network spectrum and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy storagecapacity by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of network spectrumor bandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of network spectrum andhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of energy credits by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of network spectrum and having amachine that automatically allocates its energy capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of network spectrum and having amachine that automatically allocates its networking capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a smart contract wrapper usinga distributed ledger wherein the smart contract embeds IP licensingterms for intellectual property embedded in the distributed ledger andwherein executing an operation on the distributed ledger provides accessto the intellectual property and commits the executing party to the IPlicensing terms. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to agree to an apportionment ofroyalties among the parties in the ledger. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to commit a party to a contract term. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of network spectrum and having a distributedledger that tokenizes executable algorithmic logic, such that operationon the distributed ledger provides provable access to the executablealgorithmic logic. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a distributed ledger thattokenizes a 3D printer instruction set, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of network spectrum and having adistributed ledger that tokenizes an instruction set for a coatingprocess, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a distributed ledger thattokenizes an instruction set for a semiconductor fabrication process,such that operation on the distributed ledger provides provable accessto the fabrication process. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a distributed ledger thattokenizes a firmware program, such that operation on the distributedledger provides provable access to the firmware program. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of network spectrum and having a distributedledger that tokenizes an instruction set for an FPGA, such thatoperation on the distributed ledger provides provable access to theFPGA. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of network spectrum andhaving a distributed ledger that tokenizes serverless code logic, suchthat operation on the distributed ledger provides provable access to theserverless code logic. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a distributed ledger thattokenizes an instruction set for a crystal fabrication system, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a distributed ledger thattokenizes an instruction set for a food preparation process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a distributed ledger thattokenizes an instruction set for a polymer production process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a distributed ledger thattokenizes an instruction set for chemical synthesis process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a distributed ledger thattokenizes an instruction set for a biological production process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a distributed ledger thattokenizes a trade secret with an expert wrapper, such that operation onthe distributed ledger provides provable access to the trade secret andthe wrapper provides validation of the trade secret by the expert. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of network spectrum and having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of network spectrum and having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of network spectrum and having a distributedledger that tokenizes an item of intellectual property and a reportingsystem that reports an analytic result based on the operations performedon the distributed ledger or the intellectual property. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of network spectrum and having a distributedledger that aggregates a set of instructions, where an operation on thedistributed ledger adds at least one instruction to a pre-existing setof instructions to provide a modified set of instructions. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of network spectrum and having asmart wrapper for management of a distributed ledger that aggregatessets of instructions, where the smart wrapper manages allocation ofinstruction sub-sets to the distributed ledger and access to theinstruction sub-sets. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a smart wrapper for acryptocurrency coin that directs execution of a transaction involvingthe coin to a geographic location based on tax treatment of at least oneof the coin and the transaction in the geographic location. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of network spectrum and having aself-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of network spectrum andhaving an expert system that uses machine learning to optimize theexecution of cryptocurrency transactions based on tax status. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of network spectrum and having anexpert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of network spectrum and having an expert systemthat uses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of network spectrum andhaving an expert system that uses machine learning to optimize theexecution of a cryptocurrency transaction based on an understanding ofavailable energy sources to power computing resources to execute thetransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of network spectrum andhaving an expert system that uses machine learning to optimize chargingand recharging cycle of a rechargeable battery system to provide energyfor execution of a cryptocurrency transaction. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having an expert system that predictsa forward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having an expert system that predictsa forward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having an expert system that predictsa forward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of network spectrum and having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of network spectrum andhaving an expert system that predicts a forward market price in anenergy market based on an understanding obtained by analyzing Internetof Things data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having an expert system that predictsa forward market price in an energy market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of network spectrum and having an expert systemthat predicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of network spectrum andhaving an expert system that predicts a forward market price in a marketfor spectrum or network bandwidth based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having an expert system that predictsa forward market price in a market for computing resources based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of network spectrum and having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having an expert system that predictsa forward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of network spectrum and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of network spectrum andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of network spectrum and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of network spectrum and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of network spectrum andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of network spectrum and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of network spectrum and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of network spectrum andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of network spectrum andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of network spectrum andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of network spectrum andhaving an expert system that predicts a forward market price in a marketfor spectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having an intelligent agent that isconfigured to solicit the attention resources of another externalintelligent agent. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a machine that automaticallypurchases attention resources in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of network spectrum and having afleet of machines that automatically aggregate purchasing in a forwardmarket for attention. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a likelihood of a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of network spectrum andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a facilityproduction outcome. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource utilizationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource outputselection among a set of available outputs. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize requisition and provisioning of availableenergy and compute resources to produce a favorable facility inputresource profile among a set of available profiles. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of network spectrum and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically aggregate data on collectiveoptimization of spot market purchases of network spectrum and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to generate an indication that a current or prospectivecustomer should be contacted about an output that can be provided by thefacility. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of network spectrum andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to at least one of an input resource, a facilityresource, an output parameter and an external condition related to theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of inputresources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically aggregate data oncollective optimization of spot market purchases of network spectrum andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of facility resources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically aggregate data on collective optimization ofspot market purchases of network spectrum and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a utilizationparameter for the output of the facility. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically aggregate data on collective optimization of spot marketpurchases of network spectrum and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of parameters received from a digital twin for thefacility.

In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute capacity on aforward market for compute capacity and having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having a fleet of machines that automaticallysell their aggregate energy storage capacity on a forward market forenergy storage capacity. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having a fleet of machines that automaticallysell their aggregate network bandwidth on a forward market for networkcapacity. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute capacity on a forward market for compute capacity andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from social media datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute capacity on a forward market for compute capacity andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from social media datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute capacity on a forward market for compute capacity andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from social media datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute capacity on a forward market for compute capacity andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from social media datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute capacity on a forward market for compute capacity andhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of compute capacity by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having a machinethat automatically executes an arbitrage strategy for purchase or saleof energy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having a machine that automatically executes anarbitrage strategy for purchase or sale of network spectrum or bandwidthby testing a spot market for compute capacity with a small transactionand rapidly executing a larger transaction based on the outcome of thesmall transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having a machine that automatically executes anarbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute capacity on a forward market for compute capacity andhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of energy credits by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having a machinethat automatically allocates its energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having a machine that automatically allocatesits compute capacity among a core task, a compute task, an energystorage task, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute capacity on aforward market for compute capacity and having a machine thatautomatically allocates its networking capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having a fleet of machines that automaticallyallocate collective energy capacity among a core task, a compute task,an energy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having a fleet ofmachines that automatically allocate collective compute capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having a fleet of machines that automaticallyallocate collective networking capacity among a core task, a computetask, an energy storage task, a data storage task and a networking task.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having a smartcontract wrapper using a distributed ledger wherein the smart contractembeds IP licensing terms for intellectual property embedded in thedistributed ledger and wherein executing an operation on the distributedledger provides access to the intellectual property and commits theexecuting party to the IP licensing terms. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having adistributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to agree to anapportionment of royalties among the parties in the ledger. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having adistributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to an aggregatestack of intellectual property. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to commit aparty to a contract term. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having a distributed ledger that tokenizes aninstruction set, such that operation on the distributed ledger providesprovable access to the instruction set. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having a distributed ledger that tokenizesexecutable algorithmic logic, such that operation on the distributedledger provides provable access to the executable algorithmic logic. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having adistributed ledger that tokenizes a 3D printer instruction set, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having a distributed ledger that tokenizes aninstruction set for a coating process, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having adistributed ledger that tokenizes an instruction set for a semiconductorfabrication process, such that operation on the distributed ledgerprovides provable access to the fabrication process. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute capacity on aforward market for compute capacity and having a distributed ledger thattokenizes a firmware program, such that operation on the distributedledger provides provable access to the firmware program. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute capacity on aforward market for compute capacity and having a distributed ledger thattokenizes an instruction set for an FPGA, such that operation on thedistributed ledger provides provable access to the FPGA. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute capacity on aforward market for compute capacity and having a distributed ledger thattokenizes serverless code logic, such that operation on the distributedledger provides provable access to the serverless code logic. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having adistributed ledger that tokenizes an instruction set for a crystalfabrication system, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute capacity on aforward market for compute capacity and having a distributed ledger thattokenizes an instruction set for a food preparation process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having a distributed ledger that tokenizes aninstruction set for a polymer production process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having adistributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute capacity on aforward market for compute capacity and having a distributed ledger thattokenizes an instruction set for a biological production process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having a distributed ledger that tokenizes atrade secret with an expert wrapper, such that operation on thedistributed ledger provides provable access to the trade secret and thewrapper provides validation of the trade secret by the expert. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute capacity on aforward market for compute capacity and having a distributed ledger thattokenizes an item of intellectual property and a reporting system thatreports an analytic result based on the operations performed on thedistributed ledger or the intellectual property. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute capacity on aforward market for compute capacity and having a distributed ledger thataggregates a set of instructions, where an operation on the distributedledger adds at least one instruction to a pre-existing set ofinstructions to provide a modified set of instructions. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute capacity on aforward market for compute capacity and having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having a smartwrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having a self-executing cryptocurrency cointhat commits a transaction upon recognizing a location-based parameterthat provides favorable tax treatment. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute capacity on a forward market for compute capacity andhaving an expert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute capacity on aforward market for compute capacity and having an expert system thatuses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute capacity on a forward market for compute capacity andhaving an expert system that uses machine learning to optimize theexecution of a cryptocurrency transaction based on an understanding ofavailable energy sources to power computing resources to execute thetransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute capacity on a forward market for compute capacity andhaving an expert system that uses machine learning to optimize chargingand recharging cycle of a rechargeable battery system to provide energyfor execution of a cryptocurrency transaction. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute capacity on aforward market for compute capacity and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing social network data sources and executes acryptocurrency transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having an expertsystem that predicts a forward market price in an energy market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having an expertsystem that predicts a forward market price in an energy market based onan understanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having an expertsystem that predicts a forward market price in a market for computingresources based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having an expert system that predicts a forwardmarket price in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having an expertsystem that predicts a forward market price in a market for computingresources based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute capacity on a forward market for compute capacity andhaving an expert system that predicts a forward market price in a marketfor advertising based on an understanding obtained by analyzing Internetof Things data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having an expert system that predicts a forwardmarket price in a market for advertising based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute capacity on aforward market for compute capacity and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having a machinethat automatically forecasts forward market pricing of network spectrumbased on information collected from automated agent behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute capacity on a forward market for compute capacity andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute capacity on aforward market for compute capacity and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having a machinethat automatically forecasts forward market pricing of network spectrumbased on information collected from business entity behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute capacity on a forward market for compute capacity andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute capacity on aforward market for compute capacity and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having a machinethat automatically forecasts forward market pricing of network spectrumbased on information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having a machinethat automatically forecasts forward market value of compute capabilitybased on information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having an expertsystem that predicts a forward market price in a market for spectrum ornetwork bandwidth based on an understanding obtained by analyzing socialdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute capacity on a forward market for compute capacity andhaving an intelligent agent that is configured to solicit the attentionresources of another external intelligent agent. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute capacity on aforward market for compute capacity and having a machine thatautomatically purchases attention resources in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute capacity on a forward market for compute capacity andhaving a fleet of machines that automatically aggregate purchasing in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a likelihood of a facility production outcome. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute capacity on aforward market for compute capacity and having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a facility production outcome. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize requisition andprovisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute capacity on a forward market for compute capacity andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimizeconfiguration of available energy and compute resources to produce afavorable facility resource configuration profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computecapacity on a forward market for compute capacity and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to at least one of aninput resource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute capacity on aforward market for compute capacity and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to a setof input resources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute capacity on a forward market for compute capacity andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a utilizationparameter for the output of the facility. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute capacity on a forward marketfor compute capacity and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of parameters received from a digital twin for thefacility.

In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically sell their aggregate computestorage capacity on a forward market for storage capacity. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computestorage capacity on a forward market for storage capacity and having afleet of machines that automatically sell their aggregate energy storagecapacity on a forward market for energy storage capacity. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computestorage capacity on a forward market for storage capacity and having afleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute storagecapacity on a forward market for storage capacity and having a machinethat automatically forecasts forward market pricing of energy pricesbased on information collected from social media data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computestorage capacity on a forward market for storage capacity and having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from social media data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically sell their aggregate computestorage capacity on a forward market for storage capacity and having amachine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically sell their aggregate computestorage capacity on a forward market for storage capacity and having amachine that automatically forecasts forward market value of computecapability based on information collected from social media datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute storage capacity on a forward market for storagecapacity and having a machine that automatically executes an arbitragestrategy for purchase or sale of compute capacity by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute storage capacity on a forward market for storagecapacity and having a machine that automatically executes an arbitragestrategy for purchase or sale of energy storage capacity by testing aspot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute storage capacity on a forward market for storagecapacity and having a machine that automatically executes an arbitragestrategy for purchase or sale of network spectrum or bandwidth bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute storage capacity on a forward market for storagecapacity and having a machine that automatically executes an arbitragestrategy for purchase or sale of energy by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computestorage capacity on a forward market for storage capacity and having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy credits by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computestorage capacity on a forward market for storage capacity and having amachine that automatically allocates its compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computestorage capacity on a forward market for storage capacity and having afleet of machines that automatically allocate collective energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a smart contract wrapper using adistributed ledger wherein the smart contract embeds IP licensing termsfor intellectual property embedded in the distributed ledger and whereinexecuting an operation on the distributed ledger provides access to theintellectual property and commits the executing party to the IPlicensing terms. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to agree to an apportionment ofroyalties among the parties in the ledger. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to commit a party to a contract term. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute storagecapacity on a forward market for storage capacity and having adistributed ledger that tokenizes executable algorithmic logic, suchthat operation on the distributed ledger provides provable access to theexecutable algorithmic logic. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a distributed ledger thattokenizes a 3D printer instruction set, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computestorage capacity on a forward market for storage capacity and having adistributed ledger that tokenizes an instruction set for a coatingprocess, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a distributed ledger thattokenizes an instruction set for a semiconductor fabrication process,such that operation on the distributed ledger provides provable accessto the fabrication process. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a distributed ledger thattokenizes a firmware program, such that operation on the distributedledger provides provable access to the firmware program. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute storagecapacity on a forward market for storage capacity and having adistributed ledger that tokenizes an instruction set for an FPGA, suchthat operation on the distributed ledger provides provable access to theFPGA. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically sell their aggregatecompute storage capacity on a forward market for storage capacity andhaving a distributed ledger that tokenizes serverless code logic, suchthat operation on the distributed ledger provides provable access to theserverless code logic. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a distributed ledger thattokenizes an instruction set for a crystal fabrication system, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a distributed ledger thattokenizes an instruction set for a food preparation process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a distributed ledger thattokenizes an instruction set for a polymer production process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a distributed ledger thattokenizes an instruction set for chemical synthesis process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a distributed ledger thattokenizes an instruction set for a biological production process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a distributed ledger thattokenizes a trade secret with an expert wrapper, such that operation onthe distributed ledger provides provable access to the trade secret andthe wrapper provides validation of the trade secret by the expert. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computestorage capacity on a forward market for storage capacity and having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computestorage capacity on a forward market for storage capacity and having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute storagecapacity on a forward market for storage capacity and having adistributed ledger that tokenizes an item of intellectual property and areporting system that reports an analytic result based on the operationsperformed on the distributed ledger or the intellectual property. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computestorage capacity on a forward market for storage capacity and having adistributed ledger that aggregates a set of instructions, where anoperation on the distributed ledger adds at least one instruction to apre-existing set of instructions to provide a modified set ofinstructions. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute storage capacity on a forward market for storagecapacity and having a smart wrapper for management of a distributedledger that aggregates sets of instructions, where the smart wrappermanages allocation of instruction sub-sets to the distributed ledger andaccess to the instruction sub-sets. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a smart wrapper for acryptocurrency coin that directs execution of a transaction involvingthe coin to a geographic location based on tax treatment of at least oneof the coin and the transaction in the geographic location. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computestorage capacity on a forward market for storage capacity and having aself-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute storage capacity on a forward market for storagecapacity and having an expert system that uses machine learning tooptimize the execution of cryptocurrency transactions based on taxstatus. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically sell their aggregatecompute storage capacity on a forward market for storage capacity andhaving an expert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute storagecapacity on a forward market for storage capacity and having an expertsystem that uses machine learning to optimize the execution of acryptocurrency transaction based on real time energy price informationfor an available energy source. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having an expert system that usesmachine learning to optimize the execution of a cryptocurrencytransaction based on an understanding of available energy sources topower computing resources to execute the transaction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute storagecapacity on a forward market for storage capacity and having an expertsystem that uses machine learning to optimize charging and rechargingcycle of a rechargeable battery system to provide energy for executionof a cryptocurrency transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute storagecapacity on a forward market for storage capacity and having an expertsystem that predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute storage capacity on a forward market for storagecapacity and having an expert system that predicts a forward marketprice in an energy market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute storagecapacity on a forward market for storage capacity and having an expertsystem that predicts a forward market price in a market for computingresources based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute storage capacity on a forward market for storagecapacity and having an expert system that predicts a forward marketprice in a market for computing resources based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute storagecapacity on a forward market for storage capacity and having an expertsystem that predicts a forward market price in a market for advertisingbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute storage capacity on a forward market for storagecapacity and having an expert system that predicts a forward marketprice in a market for advertising based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute storagecapacity on a forward market for storage capacity and having a machinethat automatically forecasts forward market pricing of network spectrumbased on information collected from automated agent behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute storage capacity on a forward market for storagecapacity and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computestorage capacity on a forward market for storage capacity and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute storage capacity on a forward market for storagecapacity and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected frombusiness entity behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute storagecapacity on a forward market for storage capacity and having a machinethat automatically forecasts forward market value of compute capabilitybased on information collected from business entity behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute storage capacity on a forward market for storagecapacity and having a machine that automatically forecasts forwardmarket pricing of energy prices based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computestorage capacity on a forward market for storage capacity and having amachine that automatically forecasts forward market pricing of energycredits based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute storage capacity on a forward market for storagecapacity and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having an expert system that predicts aforward market price in a market for spectrum or network bandwidth basedon an understanding obtained by analyzing social data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computestorage capacity on a forward market for storage capacity and having anintelligent agent that is configured to solicit the attention resourcesof another external intelligent agent. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a machine that automaticallypurchases attention resources in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computestorage capacity on a forward market for storage capacity and having afleet of machines that automatically aggregate purchasing in a forwardmarket for attention. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a likelihood of a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute storage capacity on a forward market for storagecapacity and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to predict afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource utilizationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource outputselection among a set of available outputs. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize requisition and provisioning of availableenergy and compute resources to produce a favorable facility inputresource profile among a set of available profiles. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate compute storagecapacity on a forward market for storage capacity and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computestorage capacity on a forward market for storage capacity and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to generate an indication that a current or prospectivecustomer should be contacted about an output that can be provided by thefacility. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute storage capacity on a forward market for storagecapacity and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to at least one of an input resource, afacility resource, an output parameter and an external condition relatedto the output of the facility. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of inputresources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate compute storage capacity on a forward market for storagecapacity and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate computestorage capacity on a forward market for storage capacity and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a utilizationparameter for the output of the facility. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate compute storage capacity on a forwardmarket for storage capacity and having an intelligent, flexible energyand compute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of parameters received from a digital twin for thefacility.

In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically sell their aggregate energystorage capacity on a forward market for energy storage capacity. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate energy storagecapacity on a forward market for energy storage capacity and having afleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate energy storage capacityon a forward market for energy storage capacity and having a machinethat automatically forecasts forward market pricing of energy pricesbased on information collected from social media data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate energy storagecapacity on a forward market for energy storage capacity and having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from social media data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically sell their aggregate energystorage capacity on a forward market for energy storage capacity andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from social media datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate energy storage capacity on a forward market for energy storagecapacity and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromsocial media data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate energy storagecapacity on a forward market for energy storage capacity and having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy credits by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking task.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically sell their aggregate energystorage capacity on a forward market for energy storage capacity andhaving a machine that automatically allocates its compute capacity amonga core task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a machine thatautomatically allocates its networking capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a smart contract wrapperusing a distributed ledger wherein the smart contract embeds IPlicensing terms for intellectual property embedded in the distributedledger and wherein executing an operation on the distributed ledgerprovides access to the intellectual property and commits the executingparty to the IP licensing terms. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to agree to an apportionment ofroyalties among the parties in the ledger. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to commit a party to a contract term. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate energy storage capacityon a forward market for energy storage capacity and having a distributedledger that tokenizes executable algorithmic logic, such that operationon the distributed ledger provides provable access to the executablealgorithmic logic. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a distributed ledger thattokenizes a 3D printer instruction set, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate energy storagecapacity on a forward market for energy storage capacity and having adistributed ledger that tokenizes an instruction set for a coatingprocess, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a distributed ledger thattokenizes an instruction set for a semiconductor fabrication process,such that operation on the distributed ledger provides provable accessto the fabrication process. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a distributed ledger thattokenizes a firmware program, such that operation on the distributedledger provides provable access to the firmware program. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate energy storage capacityon a forward market for energy storage capacity and having a distributedledger that tokenizes an instruction set for an FPGA, such thatoperation on the distributed ledger provides provable access to theFPGA. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically sell their aggregateenergy storage capacity on a forward market for energy storage capacityand having a distributed ledger that tokenizes serverless code logic,such that operation on the distributed ledger provides provable accessto the serverless code logic. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a distributed ledger thattokenizes an instruction set for a crystal fabrication system, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a distributed ledger thattokenizes an instruction set for a food preparation process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a distributed ledger thattokenizes an instruction set for a polymer production process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a distributed ledger thattokenizes an instruction set for chemical synthesis process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a distributed ledger thattokenizes an instruction set for a biological production process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a distributed ledger thattokenizes a trade secret with an expert wrapper, such that operation onthe distributed ledger provides provable access to the trade secret andthe wrapper provides validation of the trade secret by the expert. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate energy storagecapacity on a forward market for energy storage capacity and having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate energy storagecapacity on a forward market for energy storage capacity and having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate energy storage capacityon a forward market for energy storage capacity and having a distributedledger that tokenizes an item of intellectual property and a reportingsystem that reports an analytic result based on the operations performedon the distributed ledger or the intellectual property. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate energy storage capacityon a forward market for energy storage capacity and having a distributedledger that aggregates a set of instructions, where an operation on thedistributed ledger adds at least one instruction to a pre-existing setof instructions to provide a modified set of instructions. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate energy storagecapacity on a forward market for energy storage capacity and having asmart wrapper for management of a distributed ledger that aggregatessets of instructions, where the smart wrapper manages allocation ofinstruction sub-sets to the distributed ledger and access to theinstruction sub-sets. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a smart wrapper for acryptocurrency coin that directs execution of a transaction involvingthe coin to a geographic location based on tax treatment of at least oneof the coin and the transaction in the geographic location. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate energy storagecapacity on a forward market for energy storage capacity and having aself-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate energy storage capacity on a forward market for energy storagecapacity and having an expert system that uses machine learning tooptimize the execution of cryptocurrency transactions based on taxstatus. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically sell their aggregateenergy storage capacity on a forward market for energy storage capacityand having an expert system that aggregates regulatory informationcovering cryptocurrency transactions and automatically selects ajurisdiction for an operation based on the regulatory information. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate energy storagecapacity on a forward market for energy storage capacity and having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on real time energy price informationfor an available energy source. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having an expert system that usesmachine learning to optimize the execution of a cryptocurrencytransaction based on an understanding of available energy sources topower computing resources to execute the transaction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate energy storage capacityon a forward market for energy storage capacity and having an expertsystem that uses machine learning to optimize charging and rechargingcycle of a rechargeable battery system to provide energy for executionof a cryptocurrency transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate energy storage capacityon a forward market for energy storage capacity and having an expertsystem that predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate energy storagecapacity on a forward market for energy storage capacity and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate energy storage capacity on a forward market for energy storagecapacity and having an expert system that predicts a forward marketprice in a market based on an understanding obtained by analyzing socialnetwork data sources and executes a cryptocurrency transaction based onthe forward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate energy storagecapacity on a forward market for energy storage capacity and having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate energy storage capacity on a forward market for energy storagecapacity and having an expert system that predicts a forward marketprice in a market for computing resources based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate energy storage capacityon a forward market for energy storage capacity and having an expertsystem that predicts a forward market price in a market for spectrum ornetwork bandwidth based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate energy storage capacity on a forward market for energy storagecapacity and having an expert system that predicts a forward marketprice in a market for advertising based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate energy storagecapacity on a forward market for energy storage capacity and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate energy storage capacity on a forward market for energy storagecapacity and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically sell their aggregate energystorage capacity on a forward market for energy storage capacity andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate energy storagecapacity on a forward market for energy storage capacity and having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate energy storage capacity on a forward market for energy storagecapacity and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected frombusiness entity behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically sell their aggregate energystorage capacity on a forward market for energy storage capacity andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate energy storage capacity on a forward market for energy storagecapacity and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate energy storagecapacity on a forward market for energy storage capacity and having amachine that automatically forecasts forward market value of computecapability based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate energy storage capacity on a forward market for energy storagecapacity and having an expert system that predicts a forward marketprice in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing social data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate energy storage capacityon a forward market for energy storage capacity and having anintelligent agent that is configured to solicit the attention resourcesof another external intelligent agent. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a machine thatautomatically purchases attention resources in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate energy storage capacity on a forward market for energy storagecapacity and having a fleet of machines that automatically aggregatepurchasing in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a likelihood of a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate energy storage capacity on a forward market for energy storagecapacity and having a system for learning on a training set of facilityoutcomes, facility parameters, and data collected from data sources totrain an artificial intelligence/machine learning system to predict afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource utilizationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource outputselection among a set of available outputs. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize requisition and provisioning of availableenergy and compute resources to produce a favorable facility inputresource profile among a set of available profiles. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate energy storage capacityon a forward market for energy storage capacity and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize configuration ofavailable energy and compute resources to produce a favorable facilityresource configuration profile among a set of available profiles. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate energy storagecapacity on a forward market for energy storage capacity and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to generate an indication that a current or prospectivecustomer should be contacted about an output that can be provided by thefacility. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate energy storage capacity on a forward market for energy storagecapacity and having an intelligent, flexible energy and compute facilitywhereby an artificial intelligence/machine learning system configuresthe facility among a set of available configurations based on a set ofdetected conditions relating to at least one of an input resource, afacility resource, an output parameter and an external condition relatedto the output of the facility. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to a setof input resources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to a setof facility resources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to anoutput parameter. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate energy storage capacity on a forwardmarket for energy storage capacity and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to autilization parameter for the output of the facility. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate energy storage capacityon a forward market for energy storage capacity and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate network bandwidth on aforward market for network capacity and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate network bandwidth on aforward market for network capacity and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate network bandwidth on aforward market for network capacity and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate network bandwidth on aforward market for network capacity and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate network bandwidth on aforward market for network capacity and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having a machine that automatically executes anarbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate network bandwidth on a forward market for network capacity andhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of network spectrum or bandwidth by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate network bandwidth on a forward market for network capacity andhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of energy by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having a machine that automatically executes anarbitrage strategy for purchase or sale of energy credits by testing aspot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate network bandwidth on a forward market for network capacity andhaving a machine that automatically allocates its energy capacity amonga core task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having a machine that automatically allocatesits compute capacity among a core task, a compute task, an energystorage task, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate network bandwidth on aforward market for network capacity and having a machine thatautomatically allocates its networking capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having a fleet of machines that automaticallyallocate collective energy capacity among a core task, a compute task,an energy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity and having a fleet ofmachines that automatically allocate collective compute capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having a fleet of machines that automaticallyallocate collective networking capacity among a core task, a computetask, an energy storage task, a data storage task and a networking task.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity and having a smartcontract wrapper using a distributed ledger wherein the smart contractembeds IP licensing terms for intellectual property embedded in thedistributed ledger and wherein executing an operation on the distributedledger provides access to the intellectual property and commits theexecuting party to the IP licensing terms. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity and having adistributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to agree to anapportionment of royalties among the parties in the ledger. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity and having adistributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to an aggregatestack of intellectual property. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to commit aparty to a contract term. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having a distributed ledger that tokenizes aninstruction set, such that operation on the distributed ledger providesprovable access to the instruction set. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having a distributed ledger that tokenizesexecutable algorithmic logic, such that operation on the distributedledger provides provable access to the executable algorithmic logic. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity and having adistributed ledger that tokenizes a 3D printer instruction set, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having a distributed ledger that tokenizes aninstruction set for a coating process, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity and having adistributed ledger that tokenizes an instruction set for a semiconductorfabrication process, such that operation on the distributed ledgerprovides provable access to the fabrication process. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate network bandwidth on aforward market for network capacity and having a distributed ledger thattokenizes a firmware program, such that operation on the distributedledger provides provable access to the firmware program. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate network bandwidth on aforward market for network capacity and having a distributed ledger thattokenizes an instruction set for an FPGA, such that operation on thedistributed ledger provides provable access to the FPGA. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate network bandwidth on aforward market for network capacity and having a distributed ledger thattokenizes serverless code logic, such that operation on the distributedledger provides provable access to the serverless code logic. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity and having adistributed ledger that tokenizes an instruction set for a crystalfabrication system, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate network bandwidth on aforward market for network capacity and having a distributed ledger thattokenizes an instruction set for a food preparation process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having a distributed ledger that tokenizes aninstruction set for a polymer production process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity and having adistributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate network bandwidth on aforward market for network capacity and having a distributed ledger thattokenizes an instruction set for a biological production process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having a distributed ledger that tokenizes atrade secret with an expert wrapper, such that operation on thedistributed ledger provides provable access to the trade secret and thewrapper provides validation of the trade secret by the expert. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity and having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity and having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate network bandwidth on aforward market for network capacity and having a distributed ledger thattokenizes an item of intellectual property and a reporting system thatreports an analytic result based on the operations performed on thedistributed ledger or the intellectual property. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate network bandwidth on aforward market for network capacity and having a distributed ledger thataggregates a set of instructions, where an operation on the distributedledger adds at least one instruction to a pre-existing set ofinstructions to provide a modified set of instructions. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate network bandwidth on aforward market for network capacity and having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity and having a smartwrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having a self-executing cryptocurrency cointhat commits a transaction upon recognizing a location-based parameterthat provides favorable tax treatment. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate network bandwidth on a forward market for network capacity andhaving an expert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate network bandwidth on aforward market for network capacity and having an expert system thatuses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate network bandwidth on a forward market for network capacity andhaving an expert system that uses machine learning to optimize theexecution of a cryptocurrency transaction based on an understanding ofavailable energy sources to power computing resources to execute thetransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate network bandwidth on a forward market for network capacity andhaving an expert system that uses machine learning to optimize chargingand recharging cycle of a rechargeable battery system to provide energyfor execution of a cryptocurrency transaction. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate network bandwidth on aforward market for network capacity and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing social network data sources and executes acryptocurrency transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity and having an expertsystem that predicts a forward market price in an energy market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity and having an expertsystem that predicts a forward market price in an energy market based onan understanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity and having an expertsystem that predicts a forward market price in a market for computingresources based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having an expert system that predicts a forwardmarket price in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity and having an expertsystem that predicts a forward market price in a market for computingresources based on an understanding obtained by analyzing social networkdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate network bandwidth on a forward market for network capacity andhaving an expert system that predicts a forward market price in a marketfor advertising based on an understanding obtained by analyzing Internetof Things data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having an expert system that predicts a forwardmarket price in a market for advertising based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate network bandwidth on aforward market for network capacity and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity and having a machinethat automatically forecasts forward market pricing of network spectrumbased on information collected from automated agent behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate network bandwidth on a forward market for network capacity andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate network bandwidth on aforward market for network capacity and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity and having a machinethat automatically forecasts forward market pricing of network spectrumbased on information collected from business entity behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate network bandwidth on a forward market for network capacity andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate network bandwidth on aforward market for network capacity and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity and having a machinethat automatically forecasts forward market pricing of network spectrumbased on information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity and having a machinethat automatically forecasts forward market pricing of energy creditsbased on information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity and having a machinethat automatically forecasts forward market value of compute capabilitybased on information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity and having an expertsystem that predicts a forward market price in a market for spectrum ornetwork bandwidth based on an understanding obtained by analyzing socialdata sources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate network bandwidth on a forward market for network capacity andhaving an intelligent agent that is configured to solicit the attentionresources of another external intelligent agent. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate network bandwidth on aforward market for network capacity and having a machine thatautomatically purchases attention resources in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate network bandwidth on a forward market for network capacity andhaving a fleet of machines that automatically aggregate purchasing in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a likelihood of a facility production outcome. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate network bandwidth on aforward market for network capacity and having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a facility production outcome. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize requisition andprovisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate network bandwidth on a forward market for network capacity andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimizeconfiguration of available energy and compute resources to produce afavorable facility resource configuration profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically sell their aggregate networkbandwidth on a forward market for network capacity and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to at least one of aninput resource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically sell their aggregate network bandwidth on aforward market for network capacity and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to a setof input resources. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically sell theiraggregate network bandwidth on a forward market for network capacity andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to an output parameter. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a utilizationparameter for the output of the facility. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically sell their aggregate network bandwidth on a forward marketfor network capacity and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of parameters received from a digital twin for thefacility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from social media data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from social media data sources andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from social media datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from socialmedia data sources and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom social media data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from social media data sources and having amachine that automatically executes an arbitrage strategy for purchaseor sale of compute capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from social media data sources andhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of energy storage capacity by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from social media data sources andhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of network spectrum or bandwidth by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from socialmedia data sources and having a machine that automatically executes anarbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from socialmedia data sources and having a machine that automatically executes anarbitrage strategy for purchase or sale of energy credits by testing aspot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from socialmedia data sources and having a machine that automatically allocates itsenergy capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from social media data sources and having amachine that automatically allocates its compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a machine thatautomatically allocates its networking capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a fleet of machinesthat automatically allocate collective energy capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a fleet of machinesthat automatically allocate collective compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a fleet of machinesthat automatically allocate collective networking capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a smart contractwrapper using a distributed ledger wherein the smart contract embeds IPlicensing terms for intellectual property embedded in the distributedledger and wherein executing an operation on the distributed ledgerprovides access to the intellectual property and commits the executingparty to the IP licensing terms. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a distributed ledgerfor aggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a distributed ledgerfor aggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to agree to an apportionment ofroyalties among the parties in the ledger. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from social media data sources and having adistributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to an aggregatestack of intellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a distributed ledgerfor aggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to commit a party to a contract term. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from social media data sources and having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a distributed ledgerthat tokenizes executable algorithmic logic, such that operation on thedistributed ledger provides provable access to the executablealgorithmic logic. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a distributed ledgerthat tokenizes a 3D printer instruction set, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from social media data sources andhaving a distributed ledger that tokenizes an instruction set for acoating process, such that operation on the distributed ledger providesprovable access to the instruction set. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a distributed ledgerthat tokenizes an instruction set for a semiconductor fabricationprocess, such that operation on the distributed ledger provides provableaccess to the fabrication process. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a distributed ledgerthat tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from social media data sources andhaving a distributed ledger that tokenizes an instruction set for anFPGA, such that operation on the distributed ledger provides provableaccess to the FPGA. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a distributed ledgerthat tokenizes serverless code logic, such that operation on thedistributed ledger provides provable access to the serverless codelogic. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from social media datasources and having a distributed ledger that tokenizes an instructionset for a crystal fabrication system, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from social media data sources andhaving a distributed ledger that tokenizes an instruction set for a foodpreparation process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from social media data sources and having adistributed ledger that tokenizes an instruction set for a polymerproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from social media data sources and having adistributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from social media data sources and having adistributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from social media data sources and having adistributed ledger that tokenizes a trade secret with an expert wrapper,such that operation on the distributed ledger provides provable accessto the trade secret and the wrapper provides validation of the tradesecret by the expert. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a distributed ledgerthat aggregates views of a trade secret into a chain that proves whichand how many parties have viewed the trade secret. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from social media data sources and having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from social media data sources and having adistributed ledger that tokenizes an item of intellectual property and areporting system that reports an analytic result based on the operationsperformed on the distributed ledger or the intellectual property. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from social media data sources andhaving a distributed ledger that aggregates a set of instructions, wherean operation on the distributed ledger adds at least one instruction toa pre-existing set of instructions to provide a modified set ofinstructions. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from socialmedia data sources and having a smart wrapper for management of adistributed ledger that aggregates sets of instructions, where the smartwrapper manages allocation of instruction sub-sets to the distributedledger and access to the instruction sub-sets. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from social media data sources and having a smartwrapper for a cryptocurrency coin that directs execution of atransaction involving the coin to a geographic location based on taxtreatment of at least one of the coin and the transaction in thegeographic location. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from social media data sources andhaving an expert system that uses machine learning to optimize theexecution of cryptocurrency transactions based on tax status. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from social media data sources andhaving an expert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from social media data sources and having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on real time energy price informationfor an available energy source. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having an expert systemthat uses machine learning to optimize the execution of a cryptocurrencytransaction based on an understanding of available energy sources topower computing resources to execute the transaction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from social media data sources and having anexpert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from social media data sources and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from social media data sources andhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from socialmedia data sources and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from social media data sources and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from socialmedia data sources and having an expert system that predicts a forwardmarket price in an energy market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having an expert systemthat predicts a forward market price in an energy market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from social media data sources andhaving an expert system that predicts a forward market price in a marketfor computing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having an expert systemthat predicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having an expert systemthat predicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from socialmedia data sources and having an expert system that predicts a forwardmarket price in a market for advertising based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from social media data sources and having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from social media data sources andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from social media data sources andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from social media data sources andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from social media data sources andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from social media data sources andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from socialmedia data sources and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom human behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from social media data sources andhaving an expert system that predicts a forward market price in a marketfor spectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having an intelligent agentthat is configured to solicit the attention resources of anotherexternal intelligent agent. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a machine thatautomatically purchases attention resources in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from socialmedia data sources and having a fleet of machines that automaticallyaggregate purchasing in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from social media data sources and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from socialmedia data sources and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energyprices based on information collected from social media data sources andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize requisitionand provisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from socialmedia data sources and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from social media data sources and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from socialmedia data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of inputresources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from socialmedia data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from socialmedia data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to an output parameter.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of energyprices based on information collected from social media data sources andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a utilization parameter for the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy prices based on information collected from socialmedia data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of parameters received from a digital twin for thefacility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of networkspectrum based on information collected from social media data sources.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of networkspectrum based on information collected from social media data sourcesand having a machine that automatically forecasts forward market pricingof energy credits based on information collected from social media datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from socialmedia data sources and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from social media data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources and having amachine that automatically executes an arbitrage strategy for purchaseor sale of compute capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from social media data sourcesand having a machine that automatically executes an arbitrage strategyfor purchase or sale of energy storage capacity by testing a spot marketfor compute capacity with a small transaction and rapidly executing alarger transaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from social media data sourcesand having a machine that automatically executes an arbitrage strategyfor purchase or sale of network spectrum or bandwidth by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from socialmedia data sources and having a machine that automatically executes anarbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from socialmedia data sources and having a machine that automatically executes anarbitrage strategy for purchase or sale of energy credits by testing aspot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from socialmedia data sources and having a machine that automatically allocates itsenergy capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources and having amachine that automatically allocates its compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having amachine that automatically allocates its networking capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having a fleetof machines that automatically allocate collective energy capacity amonga core task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having a fleetof machines that automatically allocate collective compute capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having a fleetof machines that automatically allocate collective networking capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having a smartcontract wrapper using a distributed ledger wherein the smart contractembeds IP licensing terms for intellectual property embedded in thedistributed ledger and wherein executing an operation on the distributedledger provides access to the intellectual property and commits theexecuting party to the IP licensing terms. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources and having adistributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to an aggregatestack of intellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having adistributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to agree to anapportionment of royalties among the parties in the ledger. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from social media data sourcesand having a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toan aggregate stack of intellectual property. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources and having adistributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to commit a party to a contract term. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from social media data sourcesand having a distributed ledger that tokenizes an instruction set, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having adistributed ledger that tokenizes executable algorithmic logic, suchthat operation on the distributed ledger provides provable access to theexecutable algorithmic logic. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having adistributed ledger that tokenizes a 3D printer instruction set, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having adistributed ledger that tokenizes an instruction set for a coatingprocess, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having adistributed ledger that tokenizes an instruction set for a semiconductorfabrication process, such that operation on the distributed ledgerprovides provable access to the fabrication process. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources and having adistributed ledger that tokenizes a firmware program, such thatoperation on the distributed ledger provides provable access to thefirmware program. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having adistributed ledger that tokenizes an instruction set for an FPGA, suchthat operation on the distributed ledger provides provable access to theFPGA. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from social media datasources and having a distributed ledger that tokenizes serverless codelogic, such that operation on the distributed ledger provides provableaccess to the serverless code logic. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having adistributed ledger that tokenizes an instruction set for a crystalfabrication system, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources and having adistributed ledger that tokenizes an instruction set for a foodpreparation process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources and having adistributed ledger that tokenizes an instruction set for a polymerproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources and having adistributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources and having adistributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources and having adistributed ledger that tokenizes a trade secret with an expert wrapper,such that operation on the distributed ledger provides provable accessto the trade secret and the wrapper provides validation of the tradesecret by the expert. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from social media data sourcesand having a distributed ledger that tokenizes an instruction set, suchthat operation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources and having adistributed ledger that tokenizes an item of intellectual property and areporting system that reports an analytic result based on the operationsperformed on the distributed ledger or the intellectual property. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from social media data sourcesand having a distributed ledger that aggregates a set of instructions,where an operation on the distributed ledger adds at least oneinstruction to a pre-existing set of instructions to provide a modifiedset of instructions. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having a smartwrapper for management of a distributed ledger that aggregates sets ofinstructions, where the smart wrapper manages allocation of instructionsub-sets to the distributed ledger and access to the instructionsub-sets. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from socialmedia data sources and having a smart wrapper for a cryptocurrency cointhat directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources and having aself-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from socialmedia data sources and having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from socialmedia data sources and having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on real time energy price informationfor an available energy source. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from social media data sourcesand having an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from social media data sourcesand having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from social media data sourcesand having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a cryptocurrency transaction based onthe forward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from socialmedia data sources and having an expert system that predicts a forwardmarket price in an energy market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from socialmedia data sources and having an expert system that predicts a forwardmarket price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from social media data sourcesand having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources and having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from socialmedia data sources and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources and having amachine that automatically forecasts forward market pricing of energycredits based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from socialmedia data sources and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from socialmedia data sources and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources and having amachine that automatically forecasts forward market pricing of energycredits based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from socialmedia data sources and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from socialmedia data sources and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources and having amachine that automatically forecasts forward market pricing of energycredits based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from socialmedia data sources and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources and having anexpert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having anintelligent agent that is configured to solicit the attention resourcesof another external intelligent agent. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having amachine that automatically purchases attention resources in a forwardmarket for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having a fleetof machines that automatically aggregate purchasing in a forward marketfor attention. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from socialmedia data sources and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a likelihood of a facility production outcome. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from social media data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from socialmedia data sources and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from social media data sourcesand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize requisitionand provisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from socialmedia data sources and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from socialmedia data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of inputresources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from socialmedia data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of network spectrum based on information collected from socialmedia data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to an output parameter.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of networkspectrum based on information collected from social media data sourcesand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofdetected conditions relating to a utilization parameter for the outputof the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of network spectrum based oninformation collected from social media data sources and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sources.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sourcesand having a machine that automatically forecasts forward market valueof compute capability based on information collected from social mediadata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from socialmedia data sources and having a machine that automatically executes anarbitrage strategy for purchase or sale of compute capacity by testing aspot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from socialmedia data sources and having a machine that automatically executes anarbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from socialmedia data sources and having a machine that automatically executes anarbitrage strategy for purchase or sale of network spectrum or bandwidthby testing a spot market for compute capacity with a small transactionand rapidly executing a larger transaction based on the outcome of thesmall transaction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy credits by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking task.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sourcesand having a machine that automatically allocates its compute capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a machine thatautomatically allocates its networking capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a fleet of machinesthat automatically allocate collective energy capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a fleet of machinesthat automatically allocate collective compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a fleet of machinesthat automatically allocate collective networking capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a smart contractwrapper using a distributed ledger wherein the smart contract embeds IPlicensing terms for intellectual property embedded in the distributedledger and wherein executing an operation on the distributed ledgerprovides access to the intellectual property and commits the executingparty to the IP licensing terms. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a distributed ledgerfor aggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a distributed ledgerfor aggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to agree to an apportionment ofroyalties among the parties in the ledger. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from social media data sources and having adistributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to an aggregatestack of intellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a distributed ledgerfor aggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to commit a party to a contract term. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from social media data sources and having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a distributed ledgerthat tokenizes executable algorithmic logic, such that operation on thedistributed ledger provides provable access to the executablealgorithmic logic. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a distributed ledgerthat tokenizes a 3D printer instruction set, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sourcesand having a distributed ledger that tokenizes an instruction set for acoating process, such that operation on the distributed ledger providesprovable access to the instruction set. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a distributed ledgerthat tokenizes an instruction set for a semiconductor fabricationprocess, such that operation on the distributed ledger provides provableaccess to the fabrication process. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a distributed ledgerthat tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sourcesand having a distributed ledger that tokenizes an instruction set for anFPGA, such that operation on the distributed ledger provides provableaccess to the FPGA. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a distributed ledgerthat tokenizes serverless code logic, such that operation on thedistributed ledger provides provable access to the serverless codelogic. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from social media datasources and having a distributed ledger that tokenizes an instructionset for a crystal fabrication system, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sourcesand having a distributed ledger that tokenizes an instruction set for afood preparation process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from social media data sources and having adistributed ledger that tokenizes an instruction set for a polymerproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from social media data sources and having adistributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from social media data sources and having adistributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from social media data sources and having adistributed ledger that tokenizes a trade secret with an expert wrapper,such that operation on the distributed ledger provides provable accessto the trade secret and the wrapper provides validation of the tradesecret by the expert. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a distributed ledgerthat aggregates views of a trade secret into a chain that proves whichand how many parties have viewed the trade secret. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from social media data sources and having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from social media data sources and having adistributed ledger that tokenizes an item of intellectual property and areporting system that reports an analytic result based on the operationsperformed on the distributed ledger or the intellectual property. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sourcesand having a distributed ledger that aggregates a set of instructions,where an operation on the distributed ledger adds at least oneinstruction to a pre-existing set of instructions to provide a modifiedset of instructions. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sourcesand having a smart wrapper for a cryptocurrency coin that directsexecution of a transaction involving the coin to a geographic locationbased on tax treatment of at least one of the coin and the transactionin the geographic location. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sourcesand having an expert system that uses machine learning to optimize theexecution of cryptocurrency transactions based on tax status. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sourcesand having an expert system that aggregates regulatory informationcovering cryptocurrency transactions and automatically selects ajurisdiction for an operation based on the regulatory information. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sourcesand having an expert system that uses machine learning to optimize theexecution of a cryptocurrency transaction based on real time energyprice information for an available energy source. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from social media data sources and having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sourcesand having an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sourcesand having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sourcesand having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a cryptocurrency transaction based onthe forward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having an expert systemthat predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from socialmedia data sources and having an expert system that predicts a forwardmarket price in an energy market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having an expert systemthat predicts a forward market price in an energy market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sourcesand having an expert system that predicts a forward market price in amarket for computing resources based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having an expert systemthat predicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having an expert systemthat predicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from socialmedia data sources and having an expert system that predicts a forwardmarket price in a market for advertising based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from social media data sources and having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sourcesand having a machine that automatically forecasts forward market pricingof network spectrum based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sourcesand having a machine that automatically forecasts forward market valueof compute capability based on information collected from automatedagent behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sourcesand having a machine that automatically forecasts forward market pricingof network spectrum based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sourcesand having a machine that automatically forecasts forward market valueof compute capability based on information collected from businessentity behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sourcesand having a machine that automatically forecasts forward market pricingof network spectrum based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from socialmedia data sources and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom human behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sourcesand having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing social data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having an intelligent agentthat is configured to solicit the attention resources of anotherexternal intelligent agent. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a machine thatautomatically purchases attention resources in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from socialmedia data sources and having a fleet of machines that automaticallyaggregate purchasing in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from social media data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from socialmedia data sources and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sourcesand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize requisitionand provisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from socialmedia data sources and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from socialmedia data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of inputresources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from socialmedia data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketpricing of energy credits based on information collected from socialmedia data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to an output parameter.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market pricing of energycredits based on information collected from social media data sourcesand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofdetected conditions relating to a utilization parameter for the outputof the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from social media data sources and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market value of computecapability based on information collected from social media datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from socialmedia data sources and having a machine that automatically executes anarbitrage strategy for purchase or sale of compute capacity by testing aspot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from socialmedia data sources and having a machine that automatically executes anarbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from socialmedia data sources and having a machine that automatically executes anarbitrage strategy for purchase or sale of network spectrum or bandwidthby testing a spot market for compute capacity with a small transactionand rapidly executing a larger transaction based on the outcome of thesmall transaction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy credits by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources and having amachine that automatically allocates its energy capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having amachine that automatically allocates its compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having amachine that automatically allocates its networking capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having a fleetof machines that automatically allocate collective energy capacity amonga core task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having a fleetof machines that automatically allocate collective compute capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having a fleetof machines that automatically allocate collective networking capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having a smartcontract wrapper using a distributed ledger wherein the smart contractembeds IP licensing terms for intellectual property embedded in thedistributed ledger and wherein executing an operation on the distributedledger provides access to the intellectual property and commits theexecuting party to the IP licensing terms. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources and having adistributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to an aggregatestack of intellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having adistributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to agree to anapportionment of royalties among the parties in the ledger. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market value of computecapability based on information collected from social media data sourcesand having a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toan aggregate stack of intellectual property. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources and having adistributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to commit a party to a contract term. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market value of computecapability based on information collected from social media data sourcesand having a distributed ledger that tokenizes an instruction set, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having adistributed ledger that tokenizes executable algorithmic logic, suchthat operation on the distributed ledger provides provable access to theexecutable algorithmic logic. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having adistributed ledger that tokenizes a 3D printer instruction set, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having adistributed ledger that tokenizes an instruction set for a coatingprocess, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having adistributed ledger that tokenizes an instruction set for a semiconductorfabrication process, such that operation on the distributed ledgerprovides provable access to the fabrication process. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources and having adistributed ledger that tokenizes a firmware program, such thatoperation on the distributed ledger provides provable access to thefirmware program. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having adistributed ledger that tokenizes an instruction set for an FPGA, suchthat operation on the distributed ledger provides provable access to theFPGA. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from social media datasources and having a distributed ledger that tokenizes serverless codelogic, such that operation on the distributed ledger provides provableaccess to the serverless code logic. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having adistributed ledger that tokenizes an instruction set for a crystalfabrication system, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources and having adistributed ledger that tokenizes an instruction set for a foodpreparation process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources and having adistributed ledger that tokenizes an instruction set for a polymerproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources and having adistributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources and having adistributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources and having adistributed ledger that tokenizes a trade secret with an expert wrapper,such that operation on the distributed ledger provides provable accessto the trade secret and the wrapper provides validation of the tradesecret by the expert. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having adistributed ledger that aggregates views of a trade secret into a chainthat proves which and how many parties have viewed the trade secret. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market value of computecapability based on information collected from social media data sourcesand having a distributed ledger that tokenizes an instruction set, suchthat operation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources and having adistributed ledger that tokenizes an item of intellectual property and areporting system that reports an analytic result based on the operationsperformed on the distributed ledger or the intellectual property. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market value of computecapability based on information collected from social media data sourcesand having a distributed ledger that aggregates a set of instructions,where an operation on the distributed ledger adds at least oneinstruction to a pre-existing set of instructions to provide a modifiedset of instructions. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having a smartwrapper for management of a distributed ledger that aggregates sets ofinstructions, where the smart wrapper manages allocation of instructionsub-sets to the distributed ledger and access to the instructionsub-sets. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from socialmedia data sources and having a smart wrapper for a cryptocurrency cointhat directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources and having aself-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from socialmedia data sources and having an expert system that uses machinelearning to optimize the execution of cryptocurrency transactions basedon tax status. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from socialmedia data sources and having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on real time energy price informationfor an available energy source. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market value of computecapability based on information collected from social media data sourcesand having an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market value of computecapability based on information collected from social media data sourcesand having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market value of computecapability based on information collected from social media data sourcesand having an expert system that predicts a forward market price in amarket based on an understanding obtained by analyzing Internet ofThings data sources and executes a cryptocurrency transaction based onthe forward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from socialmedia data sources and having an expert system that predicts a forwardmarket price in an energy market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from socialmedia data sources and having an expert system that predicts a forwardmarket price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market value of computecapability based on information collected from social media data sourcesand having an expert system that predicts a forward market price in amarket for spectrum or network bandwidth based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources and having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from socialmedia data sources and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources and having amachine that automatically forecasts forward market pricing of energycredits based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from socialmedia data sources and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from socialmedia data sources and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources and having amachine that automatically forecasts forward market pricing of energycredits based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from socialmedia data sources and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from socialmedia data sources and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources and having amachine that automatically forecasts forward market pricing of energycredits based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from socialmedia data sources and having a machine that automatically forecastsforward market value of compute capability based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources and having anexpert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing social data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having anintelligent agent that is configured to solicit the attention resourcesof another external intelligent agent. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having amachine that automatically purchases attention resources in a forwardmarket for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having a fleetof machines that automatically aggregate purchasing in a forward marketfor attention. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from socialmedia data sources and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a likelihood of a facility production outcome. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from social media data sources and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from socialmedia data sources and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically forecasts forward market value of computecapability based on information collected from social media data sourcesand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize requisitionand provisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from socialmedia data sources and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize selection andconfiguration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having a systemfor learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from socialmedia data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of inputresources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from socialmedia data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically forecasts forward marketvalue of compute capability based on information collected from socialmedia data sources and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to an output parameter.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically forecasts forward market value of computecapability based on information collected from social media data sourcesand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofdetected conditions relating to a utilization parameter for the outputof the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from social media data sources and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of compute capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of compute capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of compute capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having amachine that automatically executes an arbitrage strategy for purchaseor sale of network spectrum or bandwidth by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of compute capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of compute capacityby testing a spot market for compute capacity with a small transactionand rapidly executing a larger transaction based on the outcome of thesmall transaction and having a machine that automatically executes anarbitrage strategy for purchase or sale of energy credits by testing aspot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of compute capacity by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a machine that automatically allocates its energycapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of compute capacityby testing a spot market for compute capacity with a small transactionand rapidly executing a larger transaction based on the outcome of thesmall transaction and having a machine that automatically allocates itscompute capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a machine thatautomatically allocates its networking capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a fleet of machines that automatically allocatecollective energy capacity among a core task, a compute task, an energystorage task, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a fleet of machines that automatically allocatecollective networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of compute capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having asmart contract wrapper using a distributed ledger wherein the smartcontract embeds IP licensing terms for intellectual property embedded inthe distributed ledger and wherein executing an operation on thedistributed ledger provides access to the intellectual property andcommits the executing party to the IP licensing terms. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a distributed ledger for aggregating intellectualproperty licensing terms, wherein a smart contract wrapper on thedistributed ledger allows an operation on the ledger to add intellectualproperty to agree to an apportionment of royalties among the parties inthe ledger. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of compute capacity by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger for aggregating intellectualproperty licensing terms, wherein a smart contract wrapper on thedistributed ledger allows an operation on the ledger to add intellectualproperty to an aggregate stack of intellectual property. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to commit a party to a contract term. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a distributed ledgerthat tokenizes an instruction set, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of compute capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes executable algorithmic logic, suchthat operation on the distributed ledger provides provable access to theexecutable algorithmic logic. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes a 3D printerinstruction set, such that operation on the distributed ledger providesprovable access to the instruction set. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of compute capacityby testing a spot market for compute capacity with a small transactionand rapidly executing a larger transaction based on the outcome of thesmall transaction and having a distributed ledger that tokenizes aninstruction set for a coating process, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of compute capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes an instruction set for a semiconductorfabrication process, such that operation on the distributed ledgerprovides provable access to the fabrication process. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a distributed ledgerthat tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of compute capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes an instruction set for an FPGA, suchthat operation on the distributed ledger provides provable access to theFPGA. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of compute capacity by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes serverless code logic, such thatoperation on the distributed ledger provides provable access to theserverless code logic. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes aninstruction set for a crystal fabrication system, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of compute capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes an instruction set for a foodpreparation process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a distributed ledgerthat tokenizes an instruction set for a polymer production process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes aninstruction set for chemical synthesis process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of compute capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a distributed ledgerthat tokenizes a trade secret with an expert wrapper, such thatoperation on the distributed ledger provides provable access to thetrade secret and the wrapper provides validation of the trade secret bythe expert. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of compute capacity by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that aggregates views of atrade secret into a chain that proves which and how many parties haveviewed the trade secret. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes aninstruction set, such that operation on the distributed ledger providesprovable access to the instruction set and execution of the instructionset on a system results in recording a transaction in the distributedledger. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of compute capacity by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes an item of intellectual property and areporting system that reports an analytic result based on the operationsperformed on the distributed ledger or the intellectual property. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of compute capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that aggregates a set of instructions, where anoperation on the distributed ledger adds at least one instruction to apre-existing set of instructions to provide a modified set ofinstructions. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of compute capacity by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a smart wrapper for management of a distributedledger that aggregates sets of instructions, where the smart wrappermanages allocation of instruction sub-sets to the distributed ledger andaccess to the instruction sub-sets. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a smart wrapper for a cryptocurrency coin thatdirects execution of a transaction involving the coin to a geographiclocation based on tax treatment of at least one of the coin and thetransaction in the geographic location. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of compute capacityby testing a spot market for compute capacity with a small transactionand rapidly executing a larger transaction based on the outcome of thesmall transaction and having a self-executing cryptocurrency coin thatcommits a transaction upon recognizing a location-based parameter thatprovides favorable tax treatment. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having an expert system that uses machine learning tooptimize the execution of cryptocurrency transactions based on taxstatus. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of compute capacity by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anexpert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having an expert system thatuses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of compute capacity by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having an expert system that uses machine learning tooptimize the execution of a cryptocurrency transaction based on anunderstanding of available energy sources to power computing resourcesto execute the transaction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having an expert system that uses machine learning tooptimize charging and recharging cycle of a rechargeable battery systemto provide energy for execution of a cryptocurrency transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of compute capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of compute capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of compute capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of compute capacity by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having an expert system that predicts a forward marketprice in a market based on an understanding obtained by analyzing socialnetwork data sources and executes a cryptocurrency transaction based onthe forward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having an expert system that predicts a forward marketprice in an energy market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of compute capacityby testing a spot market for compute capacity with a small transactionand rapidly executing a larger transaction based on the outcome of thesmall transaction and having an expert system that predicts a forwardmarket price in an energy market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having an expert system that predicts a forward marketprice in a market for computing resources based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having an expert system that predicts a forward marketprice in a market for computing resources based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of compute capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anexpert system that predicts a forward market price in a market foradvertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a machine that automatically forecasts forwardmarket pricing of energy prices based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of compute capacityby testing a spot market for compute capacity with a small transactionand rapidly executing a larger transaction based on the outcome of thesmall transaction and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of compute capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having amachine that automatically forecasts forward market value of computecapability based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a machine that automatically forecasts forwardmarket pricing of energy prices based on information collected frombusiness entity behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of compute capacityby testing a spot market for compute capacity with a small transactionand rapidly executing a larger transaction based on the outcome of thesmall transaction and having a machine that automatically forecastsforward market pricing of network spectrum based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of compute capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having amachine that automatically forecasts forward market value of computecapability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a machine that automatically forecasts forwardmarket pricing of energy prices based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having an expert system that predicts a forward marketprice in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing social data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having an intelligent agentthat is configured to solicit the attention resources of anotherexternal intelligent agent. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a machine that automatically purchases attentionresources in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of compute capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a facility production outcome. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of compute capacityby testing a spot market for compute capacity with a small transactionand rapidly executing a larger transaction based on the outcome of thesmall transaction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of compute capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofcompute capacity by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to a setof input resources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of compute capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of compute capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of parameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having amachine that automatically executes an arbitrage strategy for purchaseor sale of network spectrum or bandwidth by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy storagecapacity by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy credits bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of energy storage capacity by testing aspot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a machine that automatically allocates its energycapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy storagecapacity by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having amachine that automatically allocates its networking capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a fleet of machines that automatically allocatecollective energy capacity among a core task, a compute task, an energystorage task, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction and having a fleet ofmachines that automatically allocate collective compute capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a fleet of machines that automatically allocatecollective networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having asmart contract wrapper using a distributed ledger wherein the smartcontract embeds IP licensing terms for intellectual property embedded inthe distributed ledger and wherein executing an operation on thedistributed ledger provides access to the intellectual property andcommits the executing party to the IP licensing terms. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction and having a distributedledger for aggregating intellectual property licensing terms, wherein asmart contract wrapper on the distributed ledger allows an operation onthe ledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a distributed ledger for aggregating intellectualproperty licensing terms, wherein a smart contract wrapper on thedistributed ledger allows an operation on the ledger to add intellectualproperty to agree to an apportionment of royalties among the parties inthe ledger. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of energy storage capacity by testing aspot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger for aggregating intellectualproperty licensing terms, wherein a smart contract wrapper on thedistributed ledger allows an operation on the ledger to add intellectualproperty to an aggregate stack of intellectual property. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction and having a distributedledger for aggregating intellectual property licensing terms, wherein asmart contract wrapper on the distributed ledger allows an operation onthe ledger to commit a party to a contract term. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction and having a distributedledger that tokenizes an instruction set, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes executable algorithmic logic, suchthat operation on the distributed ledger provides provable access to theexecutable algorithmic logic. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes a 3D printerinstruction set, such that operation on the distributed ledger providesprovable access to the instruction set. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy storagecapacity by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a distributed ledger thattokenizes an instruction set for a coating process, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of energy storage capacity by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes an instruction set for a semiconductorfabrication process, such that operation on the distributed ledgerprovides provable access to the fabrication process. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction and having a distributedledger that tokenizes a firmware program, such that operation on thedistributed ledger provides provable access to the firmware program. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes an instruction set for an FPGA, suchthat operation on the distributed ledger provides provable access to theFPGA. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of energy storage capacity by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes serverless code logic, such thatoperation on the distributed ledger provides provable access to theserverless code logic. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes aninstruction set for a crystal fabrication system, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes an instruction set for a foodpreparation process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction and having a distributedledger that tokenizes an instruction set for a polymer productionprocess, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes aninstruction set for chemical synthesis process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction and having a distributedledger that tokenizes a trade secret with an expert wrapper, such thatoperation on the distributed ledger provides provable access to thetrade secret and the wrapper provides validation of the trade secret bythe expert. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of energy storage capacity by testing aspot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that aggregates views of atrade secret into a chain that proves which and how many parties haveviewed the trade secret. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes aninstruction set, such that operation on the distributed ledger providesprovable access to the instruction set and execution of the instructionset on a system results in recording a transaction in the distributedledger. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of energy storage capacity by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes an item of intellectual property and areporting system that reports an analytic result based on the operationsperformed on the distributed ledger or the intellectual property. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that aggregates a set of instructions, where anoperation on the distributed ledger adds at least one instruction to apre-existing set of instructions to provide a modified set ofinstructions. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of energy storage capacity by testing aspot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a smart wrapper for management of a distributedledger that aggregates sets of instructions, where the smart wrappermanages allocation of instruction sub-sets to the distributed ledger andaccess to the instruction sub-sets. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a smart wrapper for a cryptocurrency coin thatdirects execution of a transaction involving the coin to a geographiclocation based on tax treatment of at least one of the coin and thetransaction in the geographic location. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy storagecapacity by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anexpert system that uses machine learning to optimize the execution ofcryptocurrency transactions based on tax status. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction and having an expertsystem that aggregates regulatory information covering cryptocurrencytransactions and automatically selects a jurisdiction for an operationbased on the regulatory information. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy storagecapacity by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having an expert system that usesmachine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of energy storage capacity by testing aspot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having an expert system that uses machine learning tooptimize the execution of a cryptocurrency transaction based on anunderstanding of available energy sources to power computing resourcesto execute the transaction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having an expert system that uses machine learning tooptimize charging and recharging cycle of a rechargeable battery systemto provide energy for execution of a cryptocurrency transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of energy storage capacity by testing aspot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having an expert system that predicts a forward marketprice in a market based on an understanding obtained by analyzing socialnetwork data sources and executes a cryptocurrency transaction based onthe forward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having an expert system that predicts a forward marketprice in an energy market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy storagecapacity by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having an expert system that predicts a forward marketprice in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anexpert system that predicts a forward market price in a market forcomputing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having an expert system that predicts a forward marketprice in a market for advertising based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy storagecapacity by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of energy storage capacity by testing aspot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy storagecapacity by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of energy storage capacity by testing aspot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected frombusiness entity behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy storagecapacity by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of energy storage capacity by testing aspot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having an expert system that predicts a forward marketprice in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing social data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction and having an intelligentagent that is configured to solicit the attention resources of anotherexternal intelligent agent. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a machine that automatically purchases attentionresources in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction and having a fleet ofmachines that automatically aggregate purchasing in a forward market forattention. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of energy storage capacity by testing aspot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a likelihood of a facility production outcome. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of energy storage capacity by testing aspot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy storage capacity by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of input resources. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy storagecapacity by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to a setof facility resources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy storage capacity bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to an output parameter. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a utilization parameter for the output of the facility. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy storage capacity by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of network spectrum or bandwidth by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of network spectrum or bandwidth by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of network spectrumor bandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy credits bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of network spectrum or bandwidth bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a machine that automatically allocates its energycapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of network spectrumor bandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of network spectrum or bandwidth by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having amachine that automatically allocates its networking capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of network spectrum orbandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of network spectrum orbandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of network spectrum orbandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of network spectrum orbandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a smart contract wrapperusing a distributed ledger wherein the smart contract embeds IPlicensing terms for intellectual property embedded in the distributedledger and wherein executing an operation on the distributed ledgerprovides access to the intellectual property and commits the executingparty to the IP licensing terms. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of network spectrum orbandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of network spectrum orbandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to agree to an apportionment ofroyalties among the parties in the ledger. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger for aggregating intellectual property licensingterms, wherein a smart contract wrapper on the distributed ledger allowsan operation on the ledger to add intellectual property to an aggregatestack of intellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of network spectrum orbandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to commit a party to a contract term. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of network spectrum orbandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a distributed ledger thattokenizes executable algorithmic logic, such that operation on thedistributed ledger provides provable access to the executablealgorithmic logic. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of network spectrum orbandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a distributed ledger thattokenizes a 3D printer instruction set, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of network spectrum or bandwidth by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes an instruction set for a coatingprocess, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of network spectrum orbandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a distributed ledger thattokenizes an instruction set for a semiconductor fabrication process,such that operation on the distributed ledger provides provable accessto the fabrication process. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of network spectrum orbandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a distributed ledger thattokenizes a firmware program, such that operation on the distributedledger provides provable access to the firmware program. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes an instruction set for an FPGA, suchthat operation on the distributed ledger provides provable access to theFPGA. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of network spectrum or bandwidth by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes serverlesscode logic, such that operation on the distributed ledger providesprovable access to the serverless code logic. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes an instruction set for a crystalfabrication system, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes an instruction set for a foodpreparation process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes an instruction set for a polymerproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes an instruction set for chemicalsynthesis process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes an instruction set for a biologicalproduction process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes a trade secret with an expert wrapper,such that operation on the distributed ledger provides provable accessto the trade secret and the wrapper provides validation of the tradesecret by the expert. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of network spectrum orbandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes an item of intellectual property and areporting system that reports an analytic result based on the operationsperformed on the distributed ledger or the intellectual property. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of network spectrum or bandwidth by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that aggregates a set of instructions, where anoperation on the distributed ledger adds at least one instruction to apre-existing set of instructions to provide a modified set ofinstructions. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of network spectrum or bandwidth bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a smart wrapper for management of a distributedledger that aggregates sets of instructions, where the smart wrappermanages allocation of instruction sub-sets to the distributed ledger andaccess to the instruction sub-sets. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of network spectrum orbandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a smart wrapper for acryptocurrency coin that directs execution of a transaction involvingthe coin to a geographic location based on tax treatment of at least oneof the coin and the transaction in the geographic location. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of network spectrum or bandwidth by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having aself-executing cryptocurrency coin that commits a transaction uponrecognizing a location-based parameter that provides favorable taxtreatment. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of network spectrum or bandwidth bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having an expert system that uses machine learning tooptimize the execution of cryptocurrency transactions based on taxstatus. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of network spectrum or bandwidth by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having an expert system that aggregates regulatoryinformation covering cryptocurrency transactions and automaticallyselects a jurisdiction for an operation based on the regulatoryinformation. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of network spectrum or bandwidth bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having an expert system that uses machine learning tooptimize the execution of a cryptocurrency transaction based on realtime energy price information for an available energy source. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of network spectrum or bandwidth by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anexpert system that uses machine learning to optimize the execution of acryptocurrency transaction based on an understanding of available energysources to power computing resources to execute the transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of network spectrum or bandwidth by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anexpert system that uses machine learning to optimize charging andrecharging cycle of a rechargeable battery system to provide energy forexecution of a cryptocurrency transaction. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of network spectrum or bandwidth by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of network spectrum or bandwidth by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anexpert system that predicts a forward market price in a market based onan understanding obtained by analyzing Internet of Things data sourcesand executes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of network spectrum or bandwidth bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having an expert system that predicts a forward marketprice in a market based on an understanding obtained by analyzing socialnetwork data sources and executes a cryptocurrency transaction based onthe forward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of network spectrum orbandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of network spectrum or bandwidth by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anexpert system that predicts a forward market price in an energy marketbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of network spectrum or bandwidth bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having an expert system that predicts a forward marketprice in a market for computing resources based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anexpert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of network spectrumor bandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of network spectrum or bandwidth bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having an expert system that predicts a forward marketprice in a market for advertising based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of network spectrumor bandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of network spectrum or bandwidth by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from automated agent behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of network spectrum or bandwidth bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of network spectrumor bandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from automated agent behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having amachine that automatically forecasts forward market value of computecapability based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of network spectrum orbandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a machine that automaticallyforecasts forward market pricing of energy prices based on informationcollected from business entity behavioral data sources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having amachine that automatically forecasts forward market pricing of networkspectrum based on information collected from business entity behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of network spectrum or bandwidth bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected frombusiness entity behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of network spectrumor bandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a machine that automaticallyforecasts forward market value of compute capability based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of network spectrum or bandwidth by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having amachine that automatically forecasts forward market pricing of energyprices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of network spectrum or bandwidth bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of network spectrum orbandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a machine that automaticallyforecasts forward market pricing of energy credits based on informationcollected from human behavioral data sources. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having amachine that automatically forecasts forward market value of computecapability based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of network spectrum or bandwidth bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having an expert system that predicts a forward marketprice in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing social data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anintelligent agent that is configured to solicit the attention resourcesof another external intelligent agent. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of network spectrumor bandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a machine that automaticallypurchases attention resources in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of network spectrum or bandwidth by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having afleet of machines that automatically aggregate purchasing in a forwardmarket for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of network spectrum orbandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a likelihood of a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of network spectrum or bandwidth bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a facility production outcome. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of network spectrumor bandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource utilizationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource output selection among a set of available outputs. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of network spectrum or bandwidth by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize requisition andprovisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of network spectrum or bandwidth bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of network spectrum orbandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize selection and configuration of an artificialintelligence system to produce a favorable facility output profile amonga set of available artificial intelligence systems and configurations.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of network spectrum or bandwidth by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of network spectrum orbandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to atleast one of an input resource, a facility resource, an output parameterand an external condition related to the output of the facility. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of network spectrum or bandwidth by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of input resources. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of network spectrumor bandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to a setof facility resources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of network spectrum orbandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to anoutput parameter. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of network spectrum orbandwidth by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to autilization parameter for the output of the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofnetwork spectrum or bandwidth by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a machine that automatically executes anarbitrage strategy for purchase or sale of energy credits by testing aspot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of energy by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having amachine that automatically allocates its energy capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a machine that automatically allocates itscompute capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a fleet of machines that automatically allocatecollective compute capacity among a core task, a compute task, an energystorage task, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a smart contract wrapper using a distributedledger wherein the smart contract embeds IP licensing terms forintellectual property embedded in the distributed ledger and whereinexecuting an operation on the distributed ledger provides access to theintellectual property and commits the executing party to the IPlicensing terms. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger for aggregating intellectualproperty licensing terms, wherein a smart contract wrapper on thedistributed ledger allows an operation on the ledger to add intellectualproperty to an aggregate stack of intellectual property. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to agree to an apportionment ofroyalties among the parties in the ledger. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger for aggregating intellectualproperty licensing terms, wherein a smart contract wrapper on thedistributed ledger allows an operation on the ledger to commit a partyto a contract term. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes aninstruction set, such that operation on the distributed ledger providesprovable access to the instruction set. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes executablealgorithmic logic, such that operation on the distributed ledgerprovides provable access to the executable algorithmic logic. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a distributed ledgerthat tokenizes a 3D printer instruction set, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a distributed ledgerthat tokenizes an instruction set for a coating process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes aninstruction set for a semiconductor fabrication process, such thatoperation on the distributed ledger provides provable access to thefabrication process. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes a firmwareprogram, such that operation on the distributed ledger provides provableaccess to the firmware program. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes aninstruction set for an FPGA, such that operation on the distributedledger provides provable access to the FPGA. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a distributed ledger thattokenizes serverless code logic, such that operation on the distributedledger provides provable access to the serverless code logic. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a distributed ledgerthat tokenizes an instruction set for a crystal fabrication system, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes aninstruction set for a food preparation process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a distributed ledgerthat tokenizes an instruction set for a polymer production process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes aninstruction set for chemical synthesis process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a distributed ledgerthat tokenizes an instruction set for a biological production process,such that operation on the distributed ledger provides provable accessto the instruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes a tradesecret with an expert wrapper, such that operation on the distributedledger provides provable access to the trade secret and the wrapperprovides validation of the trade secret by the expert. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a distributed ledger thattokenizes an instruction set, such that operation on the distributedledger provides provable access to the instruction set and execution ofthe instruction set on a system results in recording a transaction inthe distributed ledger. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes an item ofintellectual property and a reporting system that reports an analyticresult based on the operations performed on the distributed ledger orthe intellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that aggregates a set ofinstructions, where an operation on the distributed ledger adds at leastone instruction to a pre-existing set of instructions to provide amodified set of instructions. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a smart wrapper for management of a distributedledger that aggregates sets of instructions, where the smart wrappermanages allocation of instruction sub-sets to the distributed ledger andaccess to the instruction sub-sets. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a smart wrapper for a cryptocurrency coin thatdirects execution of a transaction involving the coin to a geographiclocation based on tax treatment of at least one of the coin and thetransaction in the geographic location. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a self-executing cryptocurrency coin that commitsa transaction upon recognizing a location-based parameter that providesfavorable tax treatment. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having an expert system that uses machine learning tooptimize the execution of cryptocurrency transactions based on taxstatus. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of energy by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction and having an expertsystem that aggregates regulatory information covering cryptocurrencytransactions and automatically selects a jurisdiction for an operationbased on the regulatory information. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having an expert system that uses machine learning tooptimize the execution of a cryptocurrency transaction based on realtime energy price information for an available energy source. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having an expert system thatuses machine learning to optimize the execution of a cryptocurrencytransaction based on an understanding of available energy sources topower computing resources to execute the transaction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having an expert system that usesmachine learning to optimize charging and recharging cycle of arechargeable battery system to provide energy for execution of acryptocurrency transaction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having an expert system that predicts a forward marketprice in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having an expert system that predicts a forward marketprice in a market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having an expert system that predicts a forward marketprice in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing social network data sources and executes acryptocurrency transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of energy by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anexpert system that predicts a forward market price in a market forspectrum or network bandwidth based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having an expert system that predicts a forward marketprice in a market for computing resources based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing social datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of energy by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anintelligent agent that is configured to solicit the attention resourcesof another external intelligent agent. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a machine that automatically purchases attentionresources in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a likelihood of a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of energy by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of energy by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having asystem for learning on a training set of facility outcomes, facilityparameters, and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource outputselection among a set of available outputs. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize requisition and provisioning of availableenergy and compute resources to produce a favorable facility inputresource profile among a set of available profiles. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy by testing a spot market for compute capacity with a smalltransaction and rapidly executing a larger transaction based on theoutcome of the small transaction and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize selection and configuration of an artificialintelligence system to produce a favorable facility output profile amonga set of available artificial intelligence systems and configurations.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a system for learning ona training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to generate an indication that a current or prospectivecustomer should be contacted about an output that can be provided by thefacility. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of energy by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of energy by testing a spot market forcompute capacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of input resources. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to anoutput parameter. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy by testing a spotmarket for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of parameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy credits by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy credits by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking task.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy credits by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction and having a machine thatautomatically allocates its compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a machine that automatically allocates itsnetworking capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy credits by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a fleet of machines that automatically allocatecollective compute capacity among a core task, a compute task, an energystorage task, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy credits by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a smart contract wrapper using a distributedledger wherein the smart contract embeds IP licensing terms forintellectual property embedded in the distributed ledger and whereinexecuting an operation on the distributed ledger provides access to theintellectual property and commits the executing party to the IPlicensing terms. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger for aggregating intellectualproperty licensing terms, wherein a smart contract wrapper on thedistributed ledger allows an operation on the ledger to add intellectualproperty to an aggregate stack of intellectual property. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy credits by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to agree to an apportionment ofroyalties among the parties in the ledger. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy credits by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to an aggregate stack ofintellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger for aggregating intellectualproperty licensing terms, wherein a smart contract wrapper on thedistributed ledger allows an operation on the ledger to commit a partyto a contract term. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes aninstruction set, such that operation on the distributed ledger providesprovable access to the instruction set. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy credits bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes executablealgorithmic logic, such that operation on the distributed ledgerprovides provable access to the executable algorithmic logic. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy credits by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction and having a distributedledger that tokenizes a 3D printer instruction set, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of energy credits by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes an instruction set for a coatingprocess, such that operation on the distributed ledger provides provableaccess to the instruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes aninstruction set for a semiconductor fabrication process, such thatoperation on the distributed ledger provides provable access to thefabrication process. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes a firmwareprogram, such that operation on the distributed ledger provides provableaccess to the firmware program. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes aninstruction set for an FPGA, such that operation on the distributedledger provides provable access to the FPGA. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy credits by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a distributed ledgerthat tokenizes serverless code logic, such that operation on thedistributed ledger provides provable access to the serverless codelogic. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of energy credits by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes an instruction set for a crystalfabrication system, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy credits by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a distributed ledgerthat tokenizes an instruction set for a food preparation process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes aninstruction set for a polymer production process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically executes an arbitrage strategy for purchaseor sale of energy credits by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction and having a distributedledger that tokenizes an instruction set for chemical synthesis process,such that operation on the distributed ledger provides provable accessto the instruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes aninstruction set for a biological production process, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of energy credits by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes a trade secret with an expert wrapper,such that operation on the distributed ledger provides provable accessto the trade secret and the wrapper provides validation of the tradesecret by the expert. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that aggregates views of atrade secret into a chain that proves which and how many parties haveviewed the trade secret. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a distributed ledger that tokenizes aninstruction set, such that operation on the distributed ledger providesprovable access to the instruction set and execution of the instructionset on a system results in recording a transaction in the distributedledger. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of energy credits by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having adistributed ledger that tokenizes an item of intellectual property and areporting system that reports an analytic result based on the operationsperformed on the distributed ledger or the intellectual property. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy credits by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction and having a distributedledger that aggregates a set of instructions, where an operation on thedistributed ledger adds at least one instruction to a pre-existing setof instructions to provide a modified set of instructions. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy credits by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction and having a smart wrapperfor management of a distributed ledger that aggregates sets ofinstructions, where the smart wrapper manages allocation of instructionsub-sets to the distributed ledger and access to the instructionsub-sets. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of energy credits by testing a spot marketfor compute capacity with a small transaction and rapidly executing alarger transaction based on the outcome of the small transaction andhaving a smart wrapper for a cryptocurrency coin that directs executionof a transaction involving the coin to a geographic location based ontax treatment of at least one of the coin and the transaction in thegeographic location. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a self-executing cryptocurrency coin that commitsa transaction upon recognizing a location-based parameter that providesfavorable tax treatment. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having an expert system that uses machine learning tooptimize the execution of cryptocurrency transactions based on taxstatus. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically executes an arbitrage strategy forpurchase or sale of energy credits by testing a spot market for computecapacity with a small transaction and rapidly executing a largertransaction based on the outcome of the small transaction and having anexpert system that aggregates regulatory information coveringcryptocurrency transactions and automatically selects a jurisdiction foran operation based on the regulatory information. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy credits by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having an expert system thatuses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of energy credits by testing a spot marketfor compute capacity with a small transaction and rapidly executing alarger transaction based on the outcome of the small transaction andhaving an expert system that uses machine learning to optimize theexecution of a cryptocurrency transaction based on an understanding ofavailable energy sources to power computing resources to execute thetransaction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of energy credits by testing a spot marketfor compute capacity with a small transaction and rapidly executing alarger transaction based on the outcome of the small transaction andhaving an expert system that uses machine learning to optimize chargingand recharging cycle of a rechargeable battery system to provide energyfor execution of a cryptocurrency transaction. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy credits by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy credits by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy credits by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy credits by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction and having an expertsystem that predicts a forward market price in a market based on anunderstanding obtained by analyzing social network data sources andexecutes a cryptocurrency transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of energy credits by testing a spot marketfor compute capacity with a small transaction and rapidly executing alarger transaction based on the outcome of the small transaction andhaving an expert system that predicts a forward market price in anenergy market based on an understanding obtained by analyzing Internetof Things data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having an expert system that predicts a forward marketprice in an energy market based on an understanding obtained byanalyzing social network data sources and executes a transaction basedon the forward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having an expert system that predicts a forward marketprice in a market for computing resources based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy credits by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having an expert system that predicts a forward marketprice in a market for computing resources based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy credits by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy credits by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction and having an expertsystem that predicts a forward market price in a market for advertisingbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically executes an arbitragestrategy for purchase or sale of energy credits by testing a spot marketfor compute capacity with a small transaction and rapidly executing alarger transaction based on the outcome of the small transaction andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy credits bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy credits bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromautomated agent behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy credits bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a machine that automatically forecasts forwardmarket pricing of energy prices based on information collected frombusiness entity behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy credits bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected frombusiness entity behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy credits bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected frombusiness entity behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy credits bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected frombusiness entity behavioral data sources. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy credits bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a machine that automatically forecasts forwardmarket pricing of energy prices based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a machine that automatically forecasts forwardmarket pricing of network spectrum based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a machine that automatically forecasts forwardmarket pricing of energy credits based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a machine that automatically forecasts forwardmarket value of compute capability based on information collected fromhuman behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having an expert system that predicts a forward marketprice in a market for spectrum or network bandwidth based on anunderstanding obtained by analyzing social data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy credits by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having an intelligent agentthat is configured to solicit the attention resources of anotherexternal intelligent agent. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a machine that automatically purchases attentionresources in a forward market for attention. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy credits by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy credits by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to predict a likelihood of afacility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a facility production outcome. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyexecutes an arbitrage strategy for purchase or sale of energy credits bytesting a spot market for compute capacity with a small transaction andrapidly executing a larger transaction based on the outcome of the smalltransaction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy credits by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to generate an indication that acurrent or prospective customer should be contacted about an output thatcan be provided by the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically executes an arbitrage strategy for purchase or sale ofenergy credits by testing a spot market for compute capacity with asmall transaction and rapidly executing a larger transaction based onthe outcome of the small transaction and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to a setof input resources. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically executes an arbitrage strategy for purchaseor sale of energy credits by testing a spot market for compute capacitywith a small transaction and rapidly executing a larger transactionbased on the outcome of the small transaction and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automatically executesan arbitrage strategy for purchase or sale of energy credits by testinga spot market for compute capacity with a small transaction and rapidlyexecuting a larger transaction based on the outcome of the smalltransaction and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of parameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically allocates its energy capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically allocates its compute capacity amonga core task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically allocates its networking capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a fleet of machines that automatically allocate collective energycapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a fleet of machines that automatically allocate collectivecompute capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking taskand having a fleet of machines that automatically allocate collectivenetworking capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking taskand having a smart contract wrapper using a distributed ledger whereinthe smart contract embeds IP licensing terms for intellectual propertyembedded in the distributed ledger and wherein executing an operation onthe distributed ledger provides access to the intellectual property andcommits the executing party to the IP licensing terms. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking taskand having a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toan aggregate stack of intellectual property. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking taskand having a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toagree to an apportionment of royalties among the parties in the ledger.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically allocates its energy capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its energy capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to commit aparty to a contract term. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes executable algorithmic logic,such that operation on the distributed ledger provides provable accessto the executable algorithmic logic. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes a 3D printer instruction set,such that operation on the distributed ledger provides provable accessto the instruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes an instruction set for acoating process, such that operation on the distributed ledger providesprovable access to the instruction set. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes an instruction set for asemiconductor fabrication process, such that operation on thedistributed ledger provides provable access to the fabrication process.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically allocates its energy capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes afirmware program, such that operation on the distributed ledger providesprovable access to the firmware program. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes an instruction set for anFPGA, such that operation on the distributed ledger provides provableaccess to the FPGA. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes serverless code logic, suchthat operation on the distributed ledger provides provable access to theserverless code logic. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes an instruction set for acrystal fabrication system, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking taskand having a distributed ledger that tokenizes an instruction set for afood preparation process, such that operation on the distributed ledgerprovides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking taskand having a distributed ledger that tokenizes an instruction set for apolymer production process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking taskand having a distributed ledger that tokenizes an instruction set forchemical synthesis process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking taskand having a distributed ledger that tokenizes an instruction set for abiological production process, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking taskand having a distributed ledger that tokenizes a trade secret with anexpert wrapper, such that operation on the distributed ledger providesprovable access to the trade secret and the wrapper provides validationof the trade secret by the expert. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that aggregates views of a trade secret intoa chain that proves which and how many parties have viewed the tradesecret. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically allocates its energy capacity amonga core task, a compute task, an energy storage task, a data storage taskand a networking task and having a distributed ledger that tokenizes aninstruction set, such that operation on the distributed ledger providesprovable access to the instruction set and execution of the instructionset on a system results in recording a transaction in the distributedledger. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically allocates its energy capacity amonga core task, a compute task, an energy storage task, a data storage taskand a networking task and having a distributed ledger that tokenizes anitem of intellectual property and a reporting system that reports ananalytic result based on the operations performed on the distributedledger or the intellectual property. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that aggregates a set of instructions, wherean operation on the distributed ledger adds at least one instruction toa pre-existing set of instructions to provide a modified set ofinstructions. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically allocates its energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its energy capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having a smart wrapper for a cryptocurrency cointhat directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking taskand having a self-executing cryptocurrency coin that commits atransaction upon recognizing a location-based parameter that providesfavorable tax treatment. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that uses machine learning to optimize theexecution of cryptocurrency transactions based on tax status. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its energy capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having an expert system that aggregates regulatoryinformation covering cryptocurrency transactions and automaticallyselects a jurisdiction for an operation based on the regulatoryinformation. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically allocates its energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having an expert system that usesmachine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically allocates its energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having an expert system that usesmachine learning to optimize the execution of a cryptocurrencytransaction based on an understanding of available energy sources topower computing resources to execute the transaction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking taskand having an expert system that uses machine learning to optimizecharging and recharging cycle of a rechargeable battery system toprovide energy for execution of a cryptocurrency transaction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its energy capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically allocates its energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its energy capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingsocial network data sources and executes a cryptocurrency transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that predicts a forward market price in anenergy market based on an understanding obtained by analyzing Internetof Things data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that predicts a forward market price in anenergy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that predicts a forward market price in a marketfor computing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that predicts a forward market price in a marketfor spectrum or network bandwidth based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that predicts a forward market price in a marketfor computing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that predicts a forward market price in a marketfor advertising based on an understanding obtained by analyzing Internetof Things data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that predicts a forward market price in a marketfor advertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically allocates its energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its energy capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom human behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically allocates its energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing social datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically allocates its energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having an intelligent agent thatis configured to solicit the attention resources of another externalintelligent agent. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically purchases attention resources in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a fleet of machines that automatically aggregate purchasing in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a likelihoodof a facility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a facilityproduction outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically allocates its energy capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize requisitionand provisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically allocates its energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking taskand having a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize selectionand configuration of an artificial intelligence system to produce afavorable facility output profile among a set of available artificialintelligence systems and configurations. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to generate anindication that a current or prospective customer should be contactedabout an output that can be provided by the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking taskand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofdetected conditions relating to at least one of an input resource, afacility resource, an output parameter and an external condition relatedto the output of the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a set of input resources. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically allocates its energy capacity among a core task, a computetask, an energy storage task, a data storage task and a networking taskand having an intelligent, flexible energy and compute facility wherebyan artificial intelligence/machine learning system configures thefacility among a set of available configurations based on a set ofdetected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its energy capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to an output parameter. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its energy capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its energy capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of parametersreceived from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically allocates its compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically allocates its networking capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a fleet of machines that automatically allocate collective energycapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a fleet of machines that automatically allocate collectivecompute capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically allocates its compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a fleet of machines that automaticallyallocate collective networking capacity among a core task, a computetask, an energy storage task, a data storage task and a networking task.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically allocates its compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having a smart contract wrapper using a distributedledger wherein the smart contract embeds IP licensing terms forintellectual property embedded in the distributed ledger and whereinexecuting an operation on the distributed ledger provides access to theintellectual property and commits the executing party to the IPlicensing terms. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toan aggregate stack of intellectual property. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically allocates its compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to agree to an apportionment of royalties amongthe parties in the ledger. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toan aggregate stack of intellectual property. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically allocates its compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to commit aparty to a contract term. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes executable algorithmic logic,such that operation on the distributed ledger provides provable accessto the executable algorithmic logic. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes a 3D printer instruction set,such that operation on the distributed ledger provides provable accessto the instruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes an instruction set for acoating process, such that operation on the distributed ledger providesprovable access to the instruction set. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes an instruction set for asemiconductor fabrication process, such that operation on thedistributed ledger provides provable access to the fabrication process.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically allocates its compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes afirmware program, such that operation on the distributed ledger providesprovable access to the firmware program. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes an instruction set for anFPGA, such that operation on the distributed ledger provides provableaccess to the FPGA. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes serverless code logic, suchthat operation on the distributed ledger provides provable access to theserverless code logic. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes an instruction set for acrystal fabrication system, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically allocates its compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes aninstruction set for a food preparation process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically allocates its compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes aninstruction set for a polymer production process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically allocates its compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes aninstruction set for chemical synthesis process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically allocates its compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes aninstruction set for a biological production process, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically allocates its compute capacity amonga core task, a compute task, an energy storage task, a data storage taskand a networking task and having a distributed ledger that tokenizes atrade secret with an expert wrapper, such that operation on thedistributed ledger provides provable access to the trade secret and thewrapper provides validation of the trade secret by the expert. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that aggregates views ofa trade secret into a chain that proves which and how many parties haveviewed the trade secret. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically allocates its compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes an itemof intellectual property and a reporting system that reports an analyticresult based on the operations performed on the distributed ledger orthe intellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that aggregates a set of instructions, wherean operation on the distributed ledger adds at least one instruction toa pre-existing set of instructions to provide a modified set ofinstructions. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically allocates its computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having a smart wrapper for a cryptocurrency cointhat directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically allocates its compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a self-executing cryptocurrency coin thatcommits a transaction upon recognizing a location-based parameter thatprovides favorable tax treatment. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that uses machine learning to optimize theexecution of cryptocurrency transactions based on tax status. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having an expert system that aggregates regulatoryinformation covering cryptocurrency transactions and automaticallyselects a jurisdiction for an operation based on the regulatoryinformation. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically allocates its computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an expert system thatuses machine learning to optimize the execution of a cryptocurrencytransaction based on real time energy price information for an availableenergy source. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically allocates its computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an expert system thatuses machine learning to optimize the execution of a cryptocurrencytransaction based on an understanding of available energy sources topower computing resources to execute the transaction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically allocates its compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having an expert system that uses machine learningto optimize charging and recharging cycle of a rechargeable batterysystem to provide energy for execution of a cryptocurrency transaction.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically allocates its compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically allocates its computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingsocial network data sources and executes a cryptocurrency transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that predicts a forward market price in anenergy market based on an understanding obtained by analyzing Internetof Things data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that predicts a forward market price in anenergy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that predicts a forward market price in a marketfor computing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that predicts a forward market price in a marketfor spectrum or network bandwidth based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that predicts a forward market price in a marketfor computing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that predicts a forward market price in a marketfor advertising based on an understanding obtained by analyzing Internetof Things data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that predicts a forward market price in a marketfor advertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically allocates its computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom human behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically allocates its computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing social datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically allocates its computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an intelligent agentthat is configured to solicit the attention resources of anotherexternal intelligent agent. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically purchases attention resources in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a fleet of machines that automatically aggregate purchasing in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a likelihoodof a facility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a facilityproduction outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically allocates its compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize requisitionand provisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically allocates its computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically allocates its compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to at least one of an inputresource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically allocates its compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to an output parameter. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its compute capacity among a coretask, a compute task, an energy storage task, a data storage task and anetworking task and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its compute capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of parametersreceived from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga machine that automatically allocates its networking capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a fleet of machines that automatically allocate collective energycapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a fleet of machines that automatically allocate collectivecompute capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically allocates its networking capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a fleet of machines that automaticallyallocate collective networking capacity among a core task, a computetask, an energy storage task, a data storage task and a networking task.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically allocates its networking capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having a smart contract wrapper using adistributed ledger wherein the smart contract embeds IP licensing termsfor intellectual property embedded in the distributed ledger and whereinexecuting an operation on the distributed ledger provides access to theintellectual property and commits the executing party to the IPlicensing terms. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toan aggregate stack of intellectual property. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically allocates its networking capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to agree to an apportionment of royalties amongthe parties in the ledger. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger for aggregating intellectual propertylicensing terms, wherein a smart contract wrapper on the distributedledger allows an operation on the ledger to add intellectual property toan aggregate stack of intellectual property. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically allocates its networking capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to commit aparty to a contract term. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes executable algorithmic logic,such that operation on the distributed ledger provides provable accessto the executable algorithmic logic. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes a 3D printer instruction set,such that operation on the distributed ledger provides provable accessto the instruction set. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes an instruction set for acoating process, such that operation on the distributed ledger providesprovable access to the instruction set. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes an instruction set for asemiconductor fabrication process, such that operation on thedistributed ledger provides provable access to the fabrication process.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically allocates its networking capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having a distributed ledger that tokenizes afirmware program, such that operation on the distributed ledger providesprovable access to the firmware program. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes an instruction set for anFPGA, such that operation on the distributed ledger provides provableaccess to the FPGA. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes serverless code logic, suchthat operation on the distributed ledger provides provable access to theserverless code logic. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes an instruction set for acrystal fabrication system, such that operation on the distributedledger provides provable access to the instruction set. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically allocates its networking capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes aninstruction set for a food preparation process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically allocates its networking capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having a distributed ledger that tokenizes aninstruction set for a polymer production process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically allocates its networking capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having a distributed ledger that tokenizes aninstruction set for chemical synthesis process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically allocates its networking capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having a distributed ledger that tokenizes aninstruction set for a biological production process, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a machine that automatically allocates its networking capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having a distributed ledger thattokenizes a trade secret with an expert wrapper, such that operation onthe distributed ledger provides provable access to the trade secret andthe wrapper provides validation of the trade secret by the expert. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its networking capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having a distributed ledger that aggregatesviews of a trade secret into a chain that proves which and how manyparties have viewed the trade secret. In embodiments, provided herein isa transaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that tokenizes an instruction set, such thatoperation on the distributed ledger provides provable access to theinstruction set and execution of the instruction set on a system resultsin recording a transaction in the distributed ledger. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically allocates its networking capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes an itemof intellectual property and a reporting system that reports an analyticresult based on the operations performed on the distributed ledger orthe intellectual property. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a distributed ledger that aggregates a set of instructions, wherean operation on the distributed ledger adds at least one instruction toa pre-existing set of instructions to provide a modified set ofinstructions. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically allocates its networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its networking capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having a smart wrapper for a cryptocurrencycoin that directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically allocates its networking capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a self-executing cryptocurrency coin thatcommits a transaction upon recognizing a location-based parameter thatprovides favorable tax treatment. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that uses machine learning to optimize theexecution of cryptocurrency transactions based on tax status. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its networking capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having an expert system that aggregatesregulatory information covering cryptocurrency transactions andautomatically selects a jurisdiction for an operation based on theregulatory information. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that uses machine learning to optimize theexecution of a cryptocurrency transaction based on real time energyprice information for an available energy source. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically allocates its networking capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having an expert system that uses machine learningto optimize the execution of a cryptocurrency transaction based on anunderstanding of available energy sources to power computing resourcesto execute the transaction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that uses machine learning to optimize chargingand recharging cycle of a rechargeable battery system to provide energyfor execution of a cryptocurrency transaction. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically allocates its networking capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having an expert system that predicts a forwardmarket price in a market based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that predicts a forward market price in a marketbased on an understanding obtained by analyzing social network datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically allocates its networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its networking capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically allocates its networking capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having an expert system that predicts a forwardmarket price in an energy market based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that predicts a forward market price in anenergy market based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that predicts a forward market price in a marketfor computing resources based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that predicts a forward market price in a marketfor spectrum or network bandwidth based on an understanding obtained byanalyzing Internet of Things data sources and executes a transactionbased on the forward market prediction. In embodiments, provided hereinis a transaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that predicts a forward market price in a marketfor computing resources based on an understanding obtained by analyzingsocial network data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that predicts a forward market price in a marketfor advertising based on an understanding obtained by analyzing Internetof Things data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an expert system that predicts a forward market price in a marketfor advertising based on an understanding obtained by analyzing socialnetwork data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from automated agentbehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market pricing ofnetwork spectrum based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market pricing ofenergy credits based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from business entitybehavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market pricing ofenergy prices based on information collected from human behavioral datasources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically allocates its networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its networking capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having a machine that automatically forecastsforward market pricing of energy credits based on information collectedfrom human behavioral data sources. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically forecasts forward market value ofcompute capability based on information collected from human behavioraldata sources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically allocates its networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing social datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically allocates its networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an intelligent agentthat is configured to solicit the attention resources of anotherexternal intelligent agent. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a machine that automatically purchases attention resources in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a fleet of machines that automatically aggregate purchasing in aforward market for attention. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a likelihoodof a facility production outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to predict a facilityproduction outcome. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize provisioningand allocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga machine that automatically allocates its networking capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving a system for learning on a training set of facility outcomes,facility parameters, and data collected from data sources to train anartificial intelligence/machine learning system to optimize requisitionand provisioning of available energy and compute resources to produce afavorable facility input resource profile among a set of availableprofiles. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically allocates its networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize configuration of available energy andcompute resources to produce a favorable facility resource configurationprofile among a set of available profiles. In embodiments, providedherein is a transaction-enabling system having a machine thatautomatically allocates its networking capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its networking capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system togenerate an indication that a current or prospective customer should becontacted about an output that can be provided by the facility. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its networking capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to at least one of aninput resource, a facility resource, an output parameter and an externalcondition related to the output of the facility. In embodiments,provided herein is a transaction-enabling system having a machine thatautomatically allocates its networking capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of input resources. Inembodiments, provided herein is a transaction-enabling system having amachine that automatically allocates its networking capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having an intelligent, flexible energy andcompute facility whereby an artificial intelligence/machine learningsystem configures the facility among a set of available configurationsbased on a set of detected conditions relating to a set of facilityresources. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically allocates its networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having a machine that automaticallyallocates its networking capacity among a core task, a compute task, anenergy storage task, a data storage task and a networking task andhaving an intelligent, flexible energy and compute facility whereby anartificial intelligence/machine learning system configures the facilityamong a set of available configurations based on a set of detectedconditions relating to a utilization parameter for the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a machine that automatically allocates its networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of parameters received from adigital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective energycapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a fleet of machines that automaticallyallocate collective compute capacity among a core task, a compute task,an energy storage task, a data storage task and a networking task. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a smart contract wrapper using a distributedledger wherein the smart contract embeds IP licensing terms forintellectual property embedded in the distributed ledger and whereinexecuting an operation on the distributed ledger provides access to theintellectual property and commits the executing party to the IPlicensing terms. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to agree to an apportionment ofroyalties among the parties in the ledger. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having a distributed ledger foraggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to commit a party to a contract term. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes aninstruction set, such that operation on the distributed ledger providesprovable access to the instruction set. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizesexecutable algorithmic logic, such that operation on the distributedledger provides provable access to the executable algorithmic logic. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having a distributed ledger thattokenizes a 3D printer instruction set, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having a distributed ledger thattokenizes an instruction set for a coating process, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically allocate collective energycapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a distributed ledgerthat tokenizes an instruction set for a semiconductor fabricationprocess, such that operation on the distributed ledger provides provableaccess to the fabrication process. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes afirmware program, such that operation on the distributed ledger providesprovable access to the firmware program. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes aninstruction set for an FPGA, such that operation on the distributedledger provides provable access to the FPGA. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizesserverless code logic, such that operation on the distributed ledgerprovides provable access to the serverless code logic. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective energy capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having a distributed ledger that tokenizes aninstruction set for a crystal fabrication system, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective energycapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a distributed ledgerthat tokenizes an instruction set for a food preparation process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes aninstruction set for a polymer production process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective energycapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a distributed ledgerthat tokenizes an instruction set for chemical synthesis process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes aninstruction set for a biological production process, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically allocate collective energycapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a distributed ledgerthat tokenizes a trade secret with an expert wrapper, such thatoperation on the distributed ledger provides provable access to thetrade secret and the wrapper provides validation of the trade secret bythe expert. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically allocate collectiveenergy capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task and having a distributedledger that aggregates views of a trade secret into a chain that proveswhich and how many parties have viewed the trade secret. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective energy capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having a distributed ledger that tokenizes aninstruction set, such that operation on the distributed ledger providesprovable access to the instruction set and execution of the instructionset on a system results in recording a transaction in the distributedledger. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically allocate collective energycapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a distributed ledgerthat tokenizes an item of intellectual property and a reporting systemthat reports an analytic result based on the operations performed on thedistributed ledger or the intellectual property. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective energy capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having a distributed ledger that aggregates aset of instructions, where an operation on the distributed ledger addsat least one instruction to a pre-existing set of instructions toprovide a modified set of instructions. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a smart wrapper for management of adistributed ledger that aggregates sets of instructions, where the smartwrapper manages allocation of instruction sub-sets to the distributedledger and access to the instruction sub-sets. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a smart wrapper for a cryptocurrency cointhat directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a self-executing cryptocurrency coin thatcommits a transaction upon recognizing a location-based parameter thatprovides favorable tax treatment. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having an expert system that uses machine learningto optimize the execution of cryptocurrency transactions based on taxstatus. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically allocate collective energycapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an expert system thataggregates regulatory information covering cryptocurrency transactionsand automatically selects a jurisdiction for an operation based on theregulatory information. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having an expert system that uses machine learningto optimize the execution of a cryptocurrency transaction based on realtime energy price information for an available energy source. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having an expert system that usesmachine learning to optimize the execution of a cryptocurrencytransaction based on an understanding of available energy sources topower computing resources to execute the transaction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective energy capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having an expert system that uses machinelearning to optimize charging and recharging cycle of a rechargeablebattery system to provide energy for execution of a cryptocurrencytransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically allocate collectiveenergy capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task and having an expertsystem that predicts a forward market price in a market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective energy capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective energy capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective energy capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective energy capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective energy capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having an expert system that predicts a forwardmarket price in a market for computing resources based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective energycapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective energycapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective energycapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective energycapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective energycapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective energycapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing social datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically allocate collectiveenergy capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task and having anintelligent agent that is configured to solicit the attention resourcesof another external intelligent agent. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a machine that automatically purchasesattention resources in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective energy capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having a fleet of machines that automaticallyaggregate purchasing in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective energy capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a likelihood of a facility production outcome. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective energy capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a facility production outcome. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having a system for learning on atraining set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to generate an indication that a current or prospectivecustomer should be contacted about an output that can be provided by thefacility. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically allocate collectiveenergy capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically allocate collectiveenergy capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of input resources. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective energy capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having an intelligent, flexibleenergy and compute facility whereby an artificial intelligence/machinelearning system configures the facility among a set of availableconfigurations based on a set of detected conditions relating to anoutput parameter. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective energy capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of parameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a fleet of machines that automaticallyallocate collective networking capacity among a core task, a computetask, an energy storage task, a data storage task and a networking task.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a smart contractwrapper using a distributed ledger wherein the smart contract embeds IPlicensing terms for intellectual property embedded in the distributedledger and wherein executing an operation on the distributed ledgerprovides access to the intellectual property and commits the executingparty to the IP licensing terms. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a distributed ledgerfor aggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to agree to an apportionment ofroyalties among the parties in the ledger. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a distributed ledgerfor aggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to commit a party to a contract term. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes aninstruction set, such that operation on the distributed ledger providesprovable access to the instruction set. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizesexecutable algorithmic logic, such that operation on the distributedledger provides provable access to the executable algorithmic logic. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a distributed ledgerthat tokenizes a 3D printer instruction set, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a distributed ledgerthat tokenizes an instruction set for a coating process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes aninstruction set for a semiconductor fabrication process, such thatoperation on the distributed ledger provides provable access to thefabrication process. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes afirmware program, such that operation on the distributed ledger providesprovable access to the firmware program. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes aninstruction set for an FPGA, such that operation on the distributedledger provides provable access to the FPGA. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizesserverless code logic, such that operation on the distributed ledgerprovides provable access to the serverless code logic. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective compute capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having a distributed ledger that tokenizes aninstruction set for a crystal fabrication system, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a distributed ledgerthat tokenizes an instruction set for a food preparation process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes aninstruction set for a polymer production process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a distributed ledgerthat tokenizes an instruction set for chemical synthesis process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes aninstruction set for a biological production process, such that operationon the distributed ledger provides provable access to the instructionset. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically allocate collectivecompute capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task and having a distributedledger that tokenizes a trade secret with an expert wrapper, such thatoperation on the distributed ledger provides provable access to thetrade secret and the wrapper provides validation of the trade secret bythe expert. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically allocate collectivecompute capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task and having a distributedledger that aggregates views of a trade secret into a chain that proveswhich and how many parties have viewed the trade secret. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective compute capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having a distributed ledger that tokenizes aninstruction set, such that operation on the distributed ledger providesprovable access to the instruction set and execution of the instructionset on a system results in recording a transaction in the distributedledger. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically allocate collectivecompute capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task and having a distributedledger that tokenizes an item of intellectual property and a reportingsystem that reports an analytic result based on the operations performedon the distributed ledger or the intellectual property. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective compute capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having a distributed ledger that aggregates aset of instructions, where an operation on the distributed ledger addsat least one instruction to a pre-existing set of instructions toprovide a modified set of instructions. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a smart wrapper for management of adistributed ledger that aggregates sets of instructions, where the smartwrapper manages allocation of instruction sub-sets to the distributedledger and access to the instruction sub-sets. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a smart wrapper for a cryptocurrency cointhat directs execution of a transaction involving the coin to ageographic location based on tax treatment of at least one of the coinand the transaction in the geographic location. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a self-executing cryptocurrency coin thatcommits a transaction upon recognizing a location-based parameter thatprovides favorable tax treatment. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having an expert system that uses machine learningto optimize the execution of cryptocurrency transactions based on taxstatus. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically allocate collectivecompute capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task and having an expertsystem that aggregates regulatory information covering cryptocurrencytransactions and automatically selects a jurisdiction for an operationbased on the regulatory information. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having an expert system that uses machine learningto optimize the execution of a cryptocurrency transaction based on realtime energy price information for an available energy source. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an expert system thatuses machine learning to optimize the execution of a cryptocurrencytransaction based on an understanding of available energy sources topower computing resources to execute the transaction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective compute capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having an expert system that uses machinelearning to optimize charging and recharging cycle of a rechargeablebattery system to provide energy for execution of a cryptocurrencytransaction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically allocate collectivecompute capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task and having an expertsystem that predicts a forward market price in a market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective compute capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing Internet of Things data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective compute capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having an expert system that predicts aforward market price in a market based on an understanding obtained byanalyzing social network data sources and executes a cryptocurrencytransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective compute capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective compute capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having an expert system that predicts aforward market price in an energy market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective compute capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having an expert system that predicts aforward market price in a market for computing resources based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing Internet ofThings data sources and executes a transaction based on the forwardmarket prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having an expert system that predicts a forwardmarket price in a market for computing resources based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing social datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically allocate collectivecompute capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task and having anintelligent agent that is configured to solicit the attention resourcesof another external intelligent agent. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a machine that automatically purchasesattention resources in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective compute capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having a fleet of machines that automaticallyaggregate purchasing in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective compute capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a likelihood of a facility production outcome. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective compute capacity among acore task, a compute task, an energy storage task, a data storage taskand a networking task and having a system for learning on a training setof facility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system topredict a facility production outcome. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource utilization profile among a set ofavailable profiles. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize provisioning and allocation of energy and compute resources toproduce a favorable facility resource output selection among a set ofavailable outputs. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to generate an indication that a current or prospectivecustomer should be contacted about an output that can be provided by thefacility. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically allocate collectivecompute capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically allocate collectivecompute capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of input resources. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective computecapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective compute capacity among a core task, acompute task, an energy storage task, a data storage task and anetworking task and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of parameters received from a digital twin for the facility.

In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task and having a smart contract wrapper using a distributedledger wherein the smart contract embeds IP licensing terms forintellectual property embedded in the distributed ledger and whereinexecuting an operation on the distributed ledger provides access to theintellectual property and commits the executing party to the IPlicensing terms. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a distributed ledgerfor aggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to add intellectual property to agree to an apportionment ofroyalties among the parties in the ledger. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger for aggregatingintellectual property licensing terms, wherein a smart contract wrapperon the distributed ledger allows an operation on the ledger to addintellectual property to an aggregate stack of intellectual property. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a distributed ledgerfor aggregating intellectual property licensing terms, wherein a smartcontract wrapper on the distributed ledger allows an operation on theledger to commit a party to a contract term. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes aninstruction set, such that operation on the distributed ledger providesprovable access to the instruction set. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizesexecutable algorithmic logic, such that operation on the distributedledger provides provable access to the executable algorithmic logic. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a distributed ledgerthat tokenizes a 3D printer instruction set, such that operation on thedistributed ledger provides provable access to the instruction set. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a distributed ledgerthat tokenizes an instruction set for a coating process, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes aninstruction set for a semiconductor fabrication process, such thatoperation on the distributed ledger provides provable access to thefabrication process. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes afirmware program, such that operation on the distributed ledger providesprovable access to the firmware program. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes aninstruction set for an FPGA, such that operation on the distributedledger provides provable access to the FPGA. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizesserverless code logic, such that operation on the distributed ledgerprovides provable access to the serverless code logic. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective networking capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having a distributed ledger thattokenizes an instruction set for a crystal fabrication system, such thatoperation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes aninstruction set for a food preparation process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a distributed ledgerthat tokenizes an instruction set for a polymer production process, suchthat operation on the distributed ledger provides provable access to theinstruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes aninstruction set for chemical synthesis process, such that operation onthe distributed ledger provides provable access to the instruction set.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a distributed ledgerthat tokenizes an instruction set for a biological production process,such that operation on the distributed ledger provides provable accessto the instruction set. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes a tradesecret with an expert wrapper, such that operation on the distributedledger provides provable access to the trade secret and the wrapperprovides validation of the trade secret by the expert. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective networking capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having a distributed ledger thataggregates views of a trade secret into a chain that proves which andhow many parties have viewed the trade secret. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task and having a distributed ledger that tokenizes aninstruction set, such that operation on the distributed ledger providesprovable access to the instruction set and execution of the instructionset on a system results in recording a transaction in the distributedledger. In embodiments, provided herein is a transaction-enabling systemhaving a fleet of machines that automatically allocate collectivenetworking capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task and having a distributedledger that tokenizes an item of intellectual property and a reportingsystem that reports an analytic result based on the operations performedon the distributed ledger or the intellectual property. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective networking capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having a distributed ledger thataggregates a set of instructions, where an operation on the distributedledger adds at least one instruction to a pre-existing set ofinstructions to provide a modified set of instructions. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective networking capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having a smart wrapper formanagement of a distributed ledger that aggregates sets of instructions,where the smart wrapper manages allocation of instruction sub-sets tothe distributed ledger and access to the instruction sub-sets. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a smart wrapper for acryptocurrency coin that directs execution of a transaction involvingthe coin to a geographic location based on tax treatment of at least oneof the coin and the transaction in the geographic location. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a self-executingcryptocurrency coin that commits a transaction upon recognizing alocation-based parameter that provides favorable tax treatment. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an expert system thatuses machine learning to optimize the execution of cryptocurrencytransactions based on tax status. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task and having an expert system that aggregates regulatoryinformation covering cryptocurrency transactions and automaticallyselects a jurisdiction for an operation based on the regulatoryinformation. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically allocate collectivenetworking capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task and having an expertsystem that uses machine learning to optimize the execution of acryptocurrency transaction based on real time energy price informationfor an available energy source. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task and having an expert system that uses machine learningto optimize the execution of a cryptocurrency transaction based on anunderstanding of available energy sources to power computing resourcesto execute the transaction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task and having an expert system that uses machine learningto optimize charging and recharging cycle of a rechargeable batterysystem to provide energy for execution of a cryptocurrency transaction.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective networking capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing social network data sources and executes atransaction based on the forward market prediction. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective networking capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing Internet of Things data sources and executes acryptocurrency transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an expert system thatpredicts a forward market price in a market based on an understandingobtained by analyzing social network data sources and executes acryptocurrency transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an expert system thatpredicts a forward market price in an energy market based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an expert system thatpredicts a forward market price in a market for computing resourcesbased on an understanding obtained by analyzing Internet of Things datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically allocate collectivenetworking capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task and having an expertsystem that predicts a forward market price in a market for spectrum ornetwork bandwidth based on an understanding obtained by analyzingInternet of Things data sources and executes a transaction based on theforward market prediction. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task and having an expert system that predicts a forwardmarket price in a market for computing resources based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing Internet of Things data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an expert system thatpredicts a forward market price in a market for advertising based on anunderstanding obtained by analyzing social network data sources andexecutes a transaction based on the forward market prediction. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from automated agent behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from automated agent behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from business entity behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from business entity behavioral data sources.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market pricing of energy prices based oninformation collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market pricing of network spectrum basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market pricing of energy credits basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a machine thatautomatically forecasts forward market value of compute capability basedon information collected from human behavioral data sources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an expert system thatpredicts a forward market price in a market for spectrum or networkbandwidth based on an understanding obtained by analyzing social datasources and executes a transaction based on the forward marketprediction. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically allocate collectivenetworking capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task and having anintelligent agent that is configured to solicit the attention resourcesof another external intelligent agent. In embodiments, provided hereinis a transaction-enabling system having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task and having a machine that automatically purchasesattention resources in a forward market for attention. In embodiments,provided herein is a transaction-enabling system having a fleet ofmachines that automatically allocate collective networking capacityamong a core task, a compute task, an energy storage task, a datastorage task and a networking task and having a fleet of machines thatautomatically aggregate purchasing in a forward market for attention. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to predict a likelihood of a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically allocate collectivenetworking capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to predict a facility productionoutcome. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically allocate collectivenetworking capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task and having a system forlearning on a training set of facility outcomes, facility parameters,and data collected from data sources to train an artificialintelligence/machine learning system to optimize provisioning andallocation of energy and compute resources to produce a favorablefacility resource utilization profile among a set of available profiles.In embodiments, provided herein is a transaction-enabling system havinga fleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to optimize provisioning and allocation of energy andcompute resources to produce a favorable facility resource outputselection among a set of available outputs. In embodiments, providedherein is a transaction-enabling system having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize requisition and provisioning of available energy and computeresources to produce a favorable facility input resource profile among aset of available profiles. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize configuration of available energy and compute resources toproduce a favorable facility resource configuration profile among a setof available profiles. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task and having a system for learning on a training set offacility outcomes, facility parameters, and data collected from datasources to train an artificial intelligence/machine learning system tooptimize selection and configuration of an artificial intelligencesystem to produce a favorable facility output profile among a set ofavailable artificial intelligence systems and configurations. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having a system for learningon a training set of facility outcomes, facility parameters, and datacollected from data sources to train an artificial intelligence/machinelearning system to generate an indication that a current or prospectivecustomer should be contacted about an output that can be provided by thefacility. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically allocate collectivenetworking capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to at least one of an input resource, a facility resource, anoutput parameter and an external condition related to the output of thefacility. In embodiments, provided herein is a transaction-enablingsystem having a fleet of machines that automatically allocate collectivenetworking capacity among a core task, a compute task, an energy storagetask, a data storage task and a networking task and having anintelligent, flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to a set of input resources. In embodiments, provided herein isa transaction-enabling system having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a set of facility resources. Inembodiments, provided herein is a transaction-enabling system having afleet of machines that automatically allocate collective networkingcapacity among a core task, a compute task, an energy storage task, adata storage task and a networking task and having an intelligent,flexible energy and compute facility whereby an artificialintelligence/machine learning system configures the facility among a setof available configurations based on a set of detected conditionsrelating to an output parameter. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of detected conditions relating to a utilization parameter for theoutput of the facility. In embodiments, provided herein is atransaction-enabling system having a fleet of machines thatautomatically allocate collective networking capacity among a core task,a compute task, an energy storage task, a data storage task and anetworking task and having an intelligent, flexible energy and computefacility whereby an artificial intelligence/machine learning systemconfigures the facility among a set of available configurations based ona set of parameters received from a digital twin for the facility.

An example transaction-enabling system includes a machine having a taskrequirement includes a compute task requirement, a networking taskrequirement, and/or an energy consumption task requirement, and acontroller having a number of circuits configured to functionallyexecute certain operations to execute a transaction of a resource forthe machine. The example controller includes a resource requirementcircuit structured to determine an amount of a resource for the machineto service the task requirement(s), a forward resource market circuitstructured to access a forward resource market, a resource marketcircuit structured to access a resource market, and a resourcedistribution circuit structured to execute a transaction of the resourceon the resource market and/or the forward resource market in response tothe determined amount of the resource.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system includes where the resource distribution circuit isfurther structured to adaptively improve at least one of an output ofthe machine or a resource utilization of the machine, and/or where theresource distribution circuit further includes at least one of a machinelearning component, an artificial intelligence component, or a neuralnetwork component. An example embodiment includes the resource as one ormore of a compute resource, an energy resource, and/or an energy creditresource. An example system includes where the resource requirementcircuit is further structured to determine a second amount of a secondresource for the machine to the task requirement(s), and where theresource distribution circuit is further structured to execute a firsttransaction of the first resource on one of the resource market or theforward resource market, and to execute a second transaction of thesecond resource on the other one of the resource market or the forwardresource market. An example embodiment further includes where the secondresource is a substitute resource for the first resource during at leasta portion of the operating conditions for the machine. An example systemincludes where the forward resource market includes a futures market forthe resource at a first time scale, and where the resource marketincludes a spot market for the resource and/or a futures market for theresource at a second time scale. An example system includes thetransaction having a transaction type such as: a sale of the resource; apurchase of the resource; a short sale of the resource; a call optionfor the resource; a put option for the resource; and/or any of theforegoing with regard to at least one of a substitute resource or acorrelated resource. An example system includes where the resourcedistribution circuit is further structured to determine at least one ofa substitute resource or a correlated resource, and to further executeat least one transaction of the at least one of the substitute resourceor the correlated resource. An example system includes where theresource distribution circuit is further structured to execute the atleast one transaction of the at least one of the substitute resource orthe correlated resource as a replacement for the transaction of theresource. An example system includes where the resource distributioncircuit is further structured to execute the at least one transaction ofthe at least one of the substitute resource or the correlated resourcein concert with the transaction of the resource.

An example procedure includes an operation to determine an amount of aresource for a machine to service at least one task requirement such asa compute task requirement, a networking task requirement, and/or anenergy consumption task requirement. The example procedure furtherincludes an operation to access a forward resource market and a resourcemarket, and an operation to execute a transaction of the resource on atleast one of the forward resource market or the resource market inresponse to the determined amount of the resource.

Certain further aspects of an example procedure are described following,any one or more of which may be present in certain embodiments. Anexample procedure further includes an operation to determine a secondamount of a second resource for the machine to service at least one ofthe task requirement(s), an operation to execute a first transaction ofthe first resource on one of the resource market or the forward resourcemarket, and an operation to execute a second transaction of the secondresource on the other one of the resource market or the forward resourcemarket. An example procedure further includes an operation to determinea substitute resource and/or a correlated resource, and an operation toexecute at least one transaction of the substitute resource and/or thecorrelated resource. An example procedure further includes an operationto execute the transaction of the substitute resource and/or thecorrelated resource as a replacement for the transaction of theresource. An example procedure further includes an operation to executethe transaction of the substitute resource and/or the correlatedresource in concert with the transaction of the resource. An exampleprocedure includes determining the substitute resource and/or thecorrelated resource by performing at least one operation such as:determining the correlated resource for the machine as a resource toservice alternate tasks that provide acceptable functionality for themachine; determining the correlated resource as a resource that isexpected to be correlated with the resource in regard to at least one ofa price or an availability; and/or determining the correlated resourceas a resource that is expected to have a corresponding price change withthe resource, such that a subsequent sale of the correlated resourcecombined with a spot market purchase of the resource provides for aplanned economic outcome.

Detailed embodiments of the present disclosure are disclosed herein;however, it is to be understood that the disclosed embodiments aremerely exemplary of the disclosure, which may be embodied in variousforms. Therefore, specific structural and functional details disclosedherein are not to be interpreted as limiting, but merely as a basis forthe claims and as a representative basis for teaching one skilled in theart to variously employ the present disclosure in virtually anyappropriately detailed structure.

The terms “a” or “an,” as used herein, are defined as one or more thanone. The term “another,” as used herein, is defined as at least a secondor more. The terms “including” and/or “having,” as used herein, aredefined as comprising (i.e., open transition).

While only a few embodiments of the present disclosure have been shownand described, it will be obvious to those skilled in the art that manychanges and modifications may be made thereunto without departing fromthe spirit and scope of the present disclosure as described in thefollowing claims. All patent applications and patents, both foreign anddomestic, and all other publications referenced herein are incorporatedherein in their entireties to the full extent permitted by law.

Certain operations described herein include interpreting, receiving,and/or determining one or more values, parameters, inputs, data, orother information (“receiving data”). Operations to receive datainclude, without limitation: receiving data via a user input; receivingdata over a network of any type; reading a data value from a memorylocation in communication with the receiving device; utilizing a defaultvalue as a received data value; estimating, calculating, or deriving adata value based on other information available to the receiving device;and/or updating any of these in response to a later received data value.In certain embodiments, a data value may be received by a firstoperation, and later updated by a second operation, as part of thereceiving a data value. For example, when communications are down,intermittent, or interrupted, a first receiving operation may beperformed, and when communications are restored an updated receivingoperation may be performed.

Certain logical groupings of operations herein, for example methods orprocedures of the current disclosure, are provided to illustrate aspectsof the present disclosure. Operations described herein are schematicallydescribed and/or depicted, and operations may be combined, divided,re-ordered, added, or removed in a manner consistent with the disclosureherein. It is understood that the context of an operational descriptionmay require an ordering for one or more operations, and/or an order forone or more operations may be explicitly disclosed, but the order ofoperations should be understood broadly, where any equivalent groupingof operations to provide an equivalent outcome of operations isspecifically contemplated herein. For example, if a value is used in oneoperational step, the determining of the value may be required beforethat operational step in certain contexts (e.g. where the time delay ofdata for an operation to achieve a certain effect is important), but maynot be required before that operation step in other contexts (e.g. whereusage of the value from a previous execution cycle of the operationswould be sufficient for those purposes). Accordingly, in certainembodiments an order of operations and grouping of operations asdescribed is explicitly contemplated herein, and in certain embodimentsre-ordering, subdivision, and/or different grouping of operations isexplicitly contemplated herein.

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software, program codes,and/or instructions on a processor. The present disclosure may beimplemented as a method on the machine, as a system or apparatus as partof or in relation to the machine, or as a computer program productembodied in a computer readable medium executing on one or more of themachines. In embodiments, the processor may be part of a server, cloudserver, client, network infrastructure, mobile computing platform,stationary computing platform, or other computing platform. A processormay be any kind of computational or processing device capable ofexecuting program instructions, codes, binary instructions and the like.The processor may be or may include a signal processor, digitalprocessor, embedded processor, microprocessor or any variant such as aco-processor (math co-processor, graphic co-processor, communicationco-processor and the like) and the like that may directly or indirectlyfacilitate execution of program code or program instructions storedthereon. In addition, the processor may enable execution of multipleprograms, threads, and codes. The threads may be executed simultaneouslyto enhance the performance of the processor and to facilitatesimultaneous operations of the application. By way of implementation,methods, program codes, program instructions and the like describedherein may be implemented in one or more thread. The thread may spawnother threads that may have assigned priorities associated with them;the processor may execute these threads based on priority or any otherorder based on instructions provided in the program code. The processor,or any machine utilizing one, may include non-transitory memory thatstores methods, codes, instructions and programs as described herein andelsewhere. The processor may access a non-transitory storage mediumthrough an interface that may store methods, codes, and instructions asdescribed herein and elsewhere. The storage medium associated with theprocessor for storing methods, programs, codes, program instructions orother type of instructions capable of being executed by the computing orprocessing device may include but may not be limited to one or more of aCD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache and thelike.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. The software program may be associated with aserver that may include a file server, print server, domain server,internet server, intranet server, cloud server, and other variants suchas secondary server, host server, distributed server and the like. Theserver may include one or more of memories, processors, computerreadable media, storage media, ports (physical and virtual),communication devices, and interfaces capable of accessing otherservers, clients, machines, and devices through a wired or a wirelessmedium, and the like. The methods, programs, or codes as describedherein and elsewhere may be executed by the server. In addition, otherdevices required for execution of methods as described in thisapplication may be considered as a part of the infrastructure associatedwith the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers,social networks, and the like. Additionally, this coupling and/orconnection may facilitate remote execution of program across thenetwork. The networking of some or all of these devices may facilitateparallel processing of a program or method at one or more locationwithout deviating from the scope of the disclosure. In addition, any ofthe devices attached to the server through an interface may include atleast one storage medium capable of storing methods, programs, codeand/or instructions. A central repository may provide programinstructions to be executed on different devices. In thisimplementation, the remote repository may act as a storage medium forprogram code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, internet client, intranetclient and other variants such as secondary client, host client,distributed client and the like. The client may include one or more ofmemories, processors, computer readable media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other clients, servers, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs, or codes asdescribed herein and elsewhere may be executed by the client. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers andthe like. Additionally, this coupling and/or connection may facilitateremote execution of program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe disclosure. In addition, any of the devices attached to the clientthrough an interface may include at least one storage medium capable ofstoring methods, programs, applications, code and/or instructions. Acentral repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for program code, instructions, and programs.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM and the like. The processes, methods, program codes, instructionsdescribed herein and elsewhere may be executed by one or more of thenetwork infrastructural elements. The methods and systems describedherein may be adapted for use with any kind of private, community, orhybrid cloud computing network or cloud computing environment, includingthose which involve features of software as a service (SaaS), platformas a service (PaaS), and/or infrastructure as a service (IaaS).

The methods, program codes, and instructions described herein andelsewhere may be implemented on a cellular network having multiplecells. The cellular network may either be frequency division multipleaccess (FDMA) network or code division multiple access (CDMA) network.The cellular network may include mobile devices, cell sites, basestations, repeaters, antennas, towers, and the like. The cell networkmay be a GSM, GPRS, 3G, EVDO, mesh, or other networks types.

The methods, program codes, and instructions described herein andelsewhere may be implemented on or through mobile devices. The mobiledevices may include navigation devices, cell phones, mobile phones,mobile personal digital assistants, laptops, palmtops, netbooks, pagers,electronic books readers, music players and the like. These devices mayinclude, apart from other components, a storage medium such as a flashmemory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled toexecute program codes, methods, and instructions stored thereon.Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute program codes. The mobile devices may communicate on apeer-to-peer network, mesh network, or other communications network. Theprogram code may be stored on the storage medium associated with theserver and executed by a computing device embedded within the server.The base station may include a computing device and a storage medium.The storage device may store program codes and instructions executed bythe computing devices associated with the base station.

The computer software, program codes, and/or instructions may be storedand/or accessed on machine readable media that may include: computercomponents, devices, and recording media that retain digital data usedfor computing for some interval of time; semiconductor storage known asrandom access memory (RAM); mass storage typically for more permanentstorage, such as optical discs, forms of magnetic storage like harddisks, tapes, drums, cards and other types; processor registers, cachememory, volatile memory, non-volatile memory; optical storage such asCD, DVD; removable media such as flash memory (e.g., USB sticks orkeys), floppy disks, magnetic tape, paper tape, punch cards, standaloneRAM disks, Zip drives, removable mass storage, off-line, and the like;other computer memory such as dynamic memory, static memory, read/writestorage, mutable storage, read only, random access, sequential access,location addressable, file addressable, content addressable, networkattached storage, storage area network, bar codes, magnetic ink, and thelike.

The methods and systems described herein may transform physical and/orintangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The elements described and depicted herein, including in flow charts andblock diagrams throughout the figures, imply logical boundaries betweenthe elements. However, according to software or hardware engineeringpractices, the depicted elements and the functions thereof may beimplemented on machines through computer executable media having aprocessor capable of executing program instructions stored thereon as amonolithic software structure, as standalone software modules, or asmodules that employ external routines, code, services, and so forth, orany combination of these, and all such implementations may be within thescope of the present disclosure. Examples of such machines may include,but may not be limited to, personal digital assistants, laptops,personal computers, mobile phones, other handheld computing devices,medical equipment, wired or wireless communication devices, transducers,chips, calculators, satellites, tablet PCs, electronic books, gadgets,electronic devices, devices having artificial intelligence, computingdevices, networking equipment, servers, routers and the like.Furthermore, the elements depicted in the flow chart and block diagramsor any other logical component may be implemented on a machine capableof executing program instructions. Thus, while the foregoing drawingsand descriptions set forth functional aspects of the disclosed systems,no particular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. Similarly, it will beappreciated that the various steps identified and described above may bevaried, and that the order of steps may be adapted to particularapplications of the techniques disclosed herein. All such variations andmodifications are intended to fall within the scope of this disclosure.As such, the depiction and/or description of an order for various stepsshould not be understood to require a particular order of execution forthose steps, unless required by a particular application, or explicitlystated or otherwise clear from the context.

The methods and/or processes described above, and steps associatedtherewith, may be realized in hardware, software or any combination ofhardware and software suitable for a particular application. Thehardware may include a general-purpose computer and/or dedicatedcomputing device or specific computing device or particular aspect orcomponent of a specific computing device. The processes may be realizedin one or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors or otherprogrammable device, along with internal and/or external memory. Theprocesses may also, or instead, be embodied in an application specificintegrated circuit, a programmable gate array, programmable array logic,or any other device or combination of devices that may be configured toprocess electronic signals. It will further be appreciated that one ormore of the processes may be realized as a computer executable codecapable of being executed on a machine-readable medium.

The computer executable code may be created using a structuredprogramming language such as C, an object oriented programming languagesuch as C++, or any other high-level or low-level programming language(including assembly languages, hardware description languages, anddatabase programming languages and technologies) that may be stored,compiled or interpreted to run on one of the above devices, as well asheterogeneous combinations of processors, processor architectures, orcombinations of different hardware and software, or any other machinecapable of executing program instructions.

Thus, in one aspect, methods described above and combinations thereofmay be embodied in computer executable code that, when executing on oneor more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, the means for performingthe steps associated with the processes described above may include anyof the hardware and/or software described above. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

While the disclosure has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present disclosure isnot to be limited by the foregoing examples, but is to be understood inthe broadest sense allowable by law.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosure (especially in the context of thefollowing claims) is to be construed to cover both the singular and theplural, unless otherwise indicated herein or clearly contradicted bycontext. The terms “comprising,” “having,” “including,” and “containing”are to be construed as open-ended terms (i.e., meaning “including, butnot limited to,”) unless otherwise noted. Recitation of ranges of valuesherein are merely intended to serve as a shorthand method of referringindividually to each separate value falling within the range, unlessotherwise indicated herein, and each separate value is incorporated intothe specification as if it were individually recited herein. All methodsdescribed herein may be performed in any suitable order unless otherwiseindicated herein or otherwise clearly contradicted by context. The useof any and all examples, or exemplary language (e.g., “such as”)provided herein, is intended merely to better illuminate the disclosureand does not pose a limitation on the scope of the disclosure unlessotherwise claimed. No language in the specification should be construedas indicating any non-claimed element as essential to the practice ofthe disclosure.

While the foregoing written description enables one skilled to make anduse what is considered presently to be the best mode thereof, thoseskilled in the art will understand and appreciate the existence ofvariations, combinations, and equivalents of the specific embodiment,method, and examples herein. The disclosure should therefore not belimited by the above described embodiment, method, and examples, but byall embodiments and methods within the scope and spirit of thedisclosure.

Any element in a claim that does not explicitly state “means for”performing a specified function, or “step for” performing a specifiedfunction, is not to be interpreted as a “means” or “step” clause asspecified in 35 U.S.C. § 112(f). In particular, any use of “step of” inthe claims is not intended to invoke the provision of 35 U.S.C. §112(f). The term “set” as used herein refers to a group having one ormore members.

Persons skilled in the art may appreciate that numerous designconfigurations may be possible to enjoy the functional benefits of theinventive systems. Thus, given the wide variety of configurations andarrangements of embodiments of the present invention the scope of theinvention is reflected by the breadth of the claims below rather thannarrowed by the embodiments described above.

What is claimed is:
 1. A transaction-enabling system, comprising: amachine having an associated regenerative energy facility, the machinehaving a requirement for at least one of a compute task, a networkingtask, and an energy consumption task; and a controller, comprising: anenergy requirement circuit structured to determine an amount of energyfor the machine to service the at least one of the compute task, thenetworking task, and the energy consumption task in response to therequirement for the at least one of the compute task, the networkingtask, and the energy consumption task; and an energy distributioncircuit structured to adaptively improve an energy delivery of energyproduced by the associated regenerative energy facility between the atleast one of the compute task, the networking task, and the energyconsumption task.
 2. The transaction-enabling system of claim 1, whereinthe adaptive improvement is in an allocation of energy delivered.
 3. Thetransaction-enabling system of claim 1, wherein the adaptive improvementis in a timing of energy delivery.
 4. The transaction-enabling system ofclaim 1, wherein the adaptive improvement is a total amount of energydelivered.
 5. The transaction-enabling system of claim 1, wherein theenergy consumption task comprises a core task.
 6. Thetransaction-enabling system of claim 1, wherein the controller furthercomprises an energy market circuit structured to access an energymarket.
 7. The transaction-enabling system of claim 6, wherein theenergy distribution circuit is further structured to adaptively improvethe energy delivery of the energy produced by the associatedregenerative energy facility between the compute task, the networkingtask, the energy consumption task, and a sale of the energy produced onthe energy market.
 8. The transaction-enabling system of claim 6,wherein the energy market comprises at least one of a spot market or aforward market.
 9. The transaction-enabling system of claim 6, whereinthe energy market comprises a spot market, and wherein the energy marketcircuit is further structured to test the spot market with a smalltransaction, and to rapidly execute a larger transaction based on anoutcome of the small transaction.
 10. The transaction-enabling system ofclaim 1, wherein the energy distribution circuit further comprises atleast one of a machine learning component, an artificial intelligencecomponent, or a neural network component.
 11. A method, comprising:determining an amount of energy for a machine to service at least one ofa compute task, a networking task, or an energy consumption task inresponse to a compute task requirement, a networking task requirement,and an energy consumption task requirement; and adaptively improving anenergy delivery between: the compute task, the networking task, and theenergy consumption task, wherein the energy delivery is of energyproduced by a regenerative energy facility of the machine.
 12. Themethod of claim 11, further comprising accessing an energy market, andadaptively improving the energy delivery of the energy produced by theregenerative energy facility between: the compute task, the networkingtask, the energy consumption task, and a sale of the energy produced onthe energy market.
 13. The method of claim 12, wherein the adaptiveimprovement is in an allocation of energy delivered.
 14. The method ofclaim 12, wherein the adaptive improvement is in a timing of energydelivery.
 15. The method of claim 12, wherein the adaptive improvementis a total amount of energy delivered.
 16. The method of claim 11,wherein the energy consumption task comprises a core task.
 17. Themethod of claim 12, wherein the energy market comprises a spot market.18. The method of claim 17, further comprising, testing the spot marketwith a small transaction and rapidly executing a larger transactionbased on an outcome of the small transaction.
 19. The method of claim12, wherein the energy market comprises a forward market.
 20. The methodof claim 11, wherein adaptively improving involves operation of at leastone of a machine learning component, an artificial intelligencecomponent, or a neural network component.